diff --git a/examples/data/hotu2/test_data_01_optitrack2hotu.npy b/examples/data/hotu2/test_data_01_optitrack2hotu.npy index ce6d84e3c..34ced2159 100644 Binary files a/examples/data/hotu2/test_data_01_optitrack2hotu.npy and b/examples/data/hotu2/test_data_01_optitrack2hotu.npy differ diff --git a/examples/data/hotu2/test_data_02_optitrack2hotu.npy b/examples/data/hotu2/test_data_02_optitrack2hotu.npy index 385d76449..c3055205b 100644 Binary files a/examples/data/hotu2/test_data_02_optitrack2hotu.npy and b/examples/data/hotu2/test_data_02_optitrack2hotu.npy differ diff --git a/examples/data/hotu2/test_data_03_optitrack2hotu.npy b/examples/data/hotu2/test_data_03_optitrack2hotu.npy index 2c8fd0403..7effa4a10 100644 Binary files a/examples/data/hotu2/test_data_03_optitrack2hotu.npy and b/examples/data/hotu2/test_data_03_optitrack2hotu.npy differ diff --git a/examples/data/hotu2/test_data_04_optitrack2hotu.npy b/examples/data/hotu2/test_data_04_optitrack2hotu.npy index 64465d9bb..80c8f001b 100644 Binary files a/examples/data/hotu2/test_data_04_optitrack2hotu.npy and b/examples/data/hotu2/test_data_04_optitrack2hotu.npy differ diff --git a/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidHOTU_RofuncRL.py b/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidHOTU_RofuncRL.py new file mode 100644 index 000000000..9a6c816e0 --- /dev/null +++ b/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidHOTU_RofuncRL.py @@ -0,0 +1,116 @@ +""" +HumanoidAMP (RofuncRL) +=========================== + +Humanoid backflip/walk/run/dance/hop, trained by RofuncRL +""" + +import isaacgym +import argparse + +from rofunc.config.utils import omegaconf_to_dict, get_config +from rofunc.learning.RofuncRL.tasks import Tasks +from rofunc.learning.RofuncRL.trainers import Trainers +from rofunc.learning.pre_trained_models.download import model_zoo +from rofunc.learning.utils.utils import set_seed + + +def train(custom_args): + # Config task and trainer parameters for Isaac Gym environments + args_overrides = ["task={}".format(custom_args.task), + "train=Humanoid{}RofuncRL".format(custom_args.agent.upper()), + "device_id={}".format(custom_args.sim_device), + "rl_device=cuda:{}".format(custom_args.rl_device), + "headless={}".format(custom_args.headless), + "num_envs={}".format(custom_args.num_envs)] + cfg = get_config('./learning/rl', 'config', args=args_overrides) + cfg.task.env.motion_file = custom_args.motion_file + cfg.task.env.object_motion_file = custom_args.object_motion_file + cfg.task.env.object_asset.assetFileName = custom_args.object_asset_files + cfg.task.env.object_asset.assetName = custom_args.object_asset_names + cfg.task.env.object_asset.assetSize = custom_args.object_asset_sizes + cfg_dict = omegaconf_to_dict(cfg.task) + + set_seed(cfg.train.Trainer.seed) + + # Instantiate the Isaac Gym environment + env = Tasks().task_map[custom_args.task](cfg=cfg_dict, + rl_device=cfg.rl_device, + sim_device=f'cuda:{cfg.device_id}', + graphics_device_id=cfg.device_id, + headless=cfg.headless, + virtual_screen_capture=cfg.capture_video, # TODO: check + force_render=cfg.force_render) + + # Instantiate the RL trainer + trainer = Trainers().trainer_map[custom_args.agent](cfg=cfg, + env=env, + device=cfg.rl_device, + env_name=custom_args.task) + # Start training + trainer.train() + + +def inference(custom_args): + # Config task and trainer parameters for Isaac Gym environments + task, motion_file = custom_args.task.split('_') + args_overrides = ["task={}".format(task), + "train=Humanoid{}RofuncRL".format(custom_args.agent.upper()), + "device_id={}".format(custom_args.sim_device), + "rl_device=cuda:{}".format(custom_args.rl_device), + "headless={}".format(False), + "num_envs={}".format(16)] + cfg = get_config('./learning/rl', 'config', args=args_overrides) + cfg_dict = omegaconf_to_dict(cfg.task) + + set_seed(cfg.train.Trainer.seed) + + # Instantiate the Isaac Gym environment + infer_env = Tasks().task_map[task](cfg=cfg_dict, + rl_device=cfg.rl_device, + sim_device=f'cuda:{cfg.device_id}', + graphics_device_id=cfg.device_id, + headless=cfg.headless, + virtual_screen_capture=cfg.capture_video, # TODO: check + force_render=cfg.force_render) + + # Instantiate the RL trainer + trainer = Trainers().trainer_map[custom_args.agent](cfg=cfg, + env=infer_env, + device=cfg.rl_device, + env_name=custom_args.task, + inference=True) + + # load checkpoint + if custom_args.ckpt_path is None: + custom_args.ckpt_path = model_zoo(name=f"{custom_args.task}.pth") + trainer.agent.load_ckpt(custom_args.ckpt_path) + + # Start inference + trainer.inference() + + +if __name__ == '__main__': + gpu_id = 1 + + parser = argparse.ArgumentParser() + # Available tasks: HumanoidHOTU + parser.add_argument("--task", type=str, default="HumanoidHOTU") + parser.add_argument("--agent", type=str, default="hotu") # Available agent: amp + parser.add_argument("--motion_file", type=str, default="examples/data/hotu2/test_data_01_optitrack2hotu.npy") + parser.add_argument("--object_motion_file", type=str, default="examples/data/hotu2/test_data_01_optitrack.csv") + parser.add_argument("--object_asset_names", type=str, default=["box"]) + parser.add_argument("--object_asset_files", type=str, default=["mjcf/objects/lab_box.xml"]) + parser.add_argument("--object_asset_sizes", type=str, default=[[1, 1, 1]]) + parser.add_argument("--num_envs", type=int, default=1024) + parser.add_argument("--sim_device", type=int, default=1) + parser.add_argument("--rl_device", type=int, default=gpu_id) + parser.add_argument("--headless", type=str, default="True") + parser.add_argument("--inference", action="store_true", help="turn to inference mode while adding this argument") + parser.add_argument("--ckpt_path", type=str, default=None) + custom_args = parser.parse_args() + + if not custom_args.inference: + train(custom_args) + else: + inference(custom_args) diff --git a/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidHOTU_ViewMotion.py b/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidHOTU_ViewMotion.py index 5b711a06c..3fc4678ea 100644 --- a/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidHOTU_ViewMotion.py +++ b/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidHOTU_ViewMotion.py @@ -25,6 +25,7 @@ def inference(custom_args): cfg = get_config("./learning/rl", "config", args=args_overrides) cfg.task.env.motion_file = custom_args.motion_file cfg.task.env.object_motion_file = custom_args.object_motion_file + cfg.task.env.object_asset.assetFileName = custom_args.object_asset_files cfg.task.env.object_asset.assetName = custom_args.object_asset_names cfg.task.env.object_asset.assetSize = custom_args.object_asset_sizes @@ -61,12 +62,14 @@ def inference(custom_args): # 1. test data provided by rofunc: `examples/data/hotu/*.npy` # 2. custom motion file with absolute path parser.add_argument("--motion_file", type=str, - default="examples/data/hotu2/test_data_01_optitrack2hotu.npy") + default="examples/data/hotu2/test_data_04_optitrack2hotu.npy") parser.add_argument("--object_motion_file", type=str, - default="examples/data/hotu2/test_data_01_optitrack.csv") - parser.add_argument("--object_asset_names", type=str, default=["box:marker 001", "box:marker 002", "box:marker 003", "box:marker 004"]) - # parser.add_argument("--object_asset_files", type=str, default=["Box.urdf"]) - parser.add_argument("--object_asset_sizes", type=str, default=[[0.05, 0.05, 0.05], [0.05, 0.05, 0.05], [0.05, 0.05, 0.05], [0.05, 0.05, 0.05]]) + default="examples/data/hotu2/test_data_04_optitrack.csv") + # parser.add_argument("--object_asset_names", type=str, default=["box:marker 001", "box:marker 002", "box:marker 003", "box:marker 004"]) + parser.add_argument("--object_asset_names", type=str, default=["box"]) + parser.add_argument("--object_asset_files", type=str, default=["mjcf/objects/lab_box.xml"]) + # parser.add_argument("--object_asset_sizes", type=str, default=[[0.05, 0.05, 0.05], [0.05, 0.05, 0.05], [0.05, 0.05, 0.05], [0.05, 0.05, 0.05]]) + parser.add_argument("--object_asset_sizes", type=str, default=[[1, 1, 1]]) custom_args = parser.parse_args() inference(custom_args) diff --git a/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidPhysHOI_RofuncRL.py b/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidPhysHOI_RofuncRL.py index 5aabd7e0b..99f3a2758 100644 --- a/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidPhysHOI_RofuncRL.py +++ b/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidPhysHOI_RofuncRL.py @@ -17,27 +17,26 @@ def train(custom_args): # Config task and trainer parameters for Isaac Gym environments - task, motion_file = custom_args.task.split('_') - args_overrides = ["task={}".format(task), - "train={}{}RofuncRL".format(task, custom_args.agent.upper()), + args_overrides = ["task={}".format(custom_args.task), + "train=BaseTask{}RofuncRL".format(custom_args.agent.upper()), "device_id={}".format(custom_args.sim_device), "rl_device=cuda:{}".format(custom_args.rl_device), "headless={}".format(custom_args.headless), "num_envs={}".format(custom_args.num_envs)] cfg = get_config('./learning/rl', 'config', args=args_overrides) - cfg.task.env.motion_file = f'amp_humanoid_{motion_file}.npy' + cfg.task.env.motion_file = custom_args.motion_file cfg_dict = omegaconf_to_dict(cfg.task) set_seed(cfg.train.Trainer.seed) # Instantiate the Isaac Gym environment - env = Tasks().task_map[task](cfg=cfg_dict, - rl_device=cfg.rl_device, - sim_device=f'cuda:{cfg.device_id}', - graphics_device_id=cfg.device_id, - headless=cfg.headless, - virtual_screen_capture=cfg.capture_video, # TODO: check - force_render=cfg.force_render) + env = Tasks().task_map[custom_args.task](cfg=cfg_dict, + rl_device=cfg.rl_device, + sim_device=f'cuda:{cfg.device_id}', + graphics_device_id=cfg.device_id, + headless=cfg.headless, + virtual_screen_capture=cfg.capture_video, # TODO: check + force_render=cfg.force_render) # Instantiate the RL trainer trainer = Trainers().trainer_map[custom_args.agent](cfg=cfg, @@ -91,10 +90,13 @@ def inference(custom_args): gpu_id = 0 parser = argparse.ArgumentParser() - # Available tasks: HumanoidAMP_backflip, HumanoidAMP_walk, HumanoidAMP_run, HumanoidAMP_dance, HumanoidAMP_hop - parser.add_argument("--task", type=str, default="HumanoidAMP_hop") - parser.add_argument("--agent", type=str, default="amp") # Available agent: amp - parser.add_argument("--num_envs", type=int, default=4096) + # Available tasks: HumanoidPhyshoi + parser.add_argument("--task", type=str, default="HumanoidPhysHOI") + # Available motion files: backdribble, backspin, changeleg, fingerspin, pass, rebound, toss, walkpick + parser.add_argument("--motion_file", type=str, default="examples/data/ballplay/toss.pt") + # Available agent: physhoi + parser.add_argument("--agent", type=str, default="physhoi") + parser.add_argument("--num_envs", type=int, default=2048) parser.add_argument("--sim_device", type=int, default=0) parser.add_argument("--rl_device", type=int, default=gpu_id) parser.add_argument("--headless", type=str, default="True") diff --git a/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidPhysHOI_ViewMotion.py b/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidPhysHOI_ViewMotion.py new file mode 100644 index 000000000..2a9ecad9f --- /dev/null +++ b/examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidPhysHOI_ViewMotion.py @@ -0,0 +1,62 @@ +""" +HumanoidPhysHOI Motion View (RofuncRL) +=========================== + +Preview the motion of the digital humanoid +""" + +import isaacgym +import argparse + +from rofunc.config.utils import omegaconf_to_dict, get_config +from rofunc.learning.RofuncRL.tasks import Tasks +from rofunc.learning.RofuncRL.trainers import Trainers + + +def inference(custom_args): + # Config task and trainer parameters for Isaac Gym environments + args_overrides = [ + f"task=HumanoidPhysHOIViewMotion", + "train=BaseTaskPHYSHOIRofuncRL", + f"device_id=0", + f"rl_device=cuda:{gpu_id}", + "headless={}".format(False), + "num_envs={}".format(1), + ] + cfg = get_config("./learning/rl", "config", args=args_overrides) + cfg.task.env.motion_file = custom_args.motion_file + cfg.task.env.playdataset = True + + cfg_dict = omegaconf_to_dict(cfg.task) + + # Instantiate the Isaac Gym environment + infer_env = Tasks().task_map[custom_args.task](cfg=cfg_dict, + rl_device=cfg.rl_device, + sim_device=f'cuda:{cfg.device_id}', + graphics_device_id=cfg.device_id, + headless=cfg.headless, + virtual_screen_capture=cfg.capture_video, # TODO: check + force_render=cfg.force_render) + + # Instantiate the RL trainer + trainer = Trainers().trainer_map["physhoi"](cfg=cfg, + env=infer_env, + device=cfg.rl_device, + env_name=custom_args.task, + inference=True) + + # Start inference + trainer.inference() + + +if __name__ == "__main__": + gpu_id = 0 + + parser = argparse.ArgumentParser() + # Available tasks: HumanoidPhyshoi + parser.add_argument("--task", type=str, default="HumanoidPhysHOI") + # Available motion files: backdribble, backspin, changeleg, fingerspin, pass, rebound, toss, walkpick + parser.add_argument("--motion_file", type=str, default="examples/data/ballplay/backdribble.pt") + custom_args = parser.parse_args() + + inference(custom_args) diff --git a/examples/simulator/example_LLM_robotics.py b/examples/simulator/example_LLM_robotics.py index c175eeb00..673c5c792 100644 --- a/examples/simulator/example_LLM_robotics.py +++ b/examples/simulator/example_LLM_robotics.py @@ -10,12 +10,12 @@ import rofunc as rf -traj_l = np.load('../data/LQT_LQR/taichi_1l.npy') -traj_l[:, 0] += 0.2 -traj_l[:, 1] = -traj_l[:, 1] -traj_l[:, 3:] = [1, 0, 0, 0] -# target_pose = traj_l[10] -traj_l = traj_l[::20] +# traj_l = np.load('../data/LQT_LQR/taichi_1l.npy') +# traj_l[:, 0] += 0.2 +# traj_l[:, 1] = -traj_l[:, 1] +# traj_l[:, 3:] = [1, 0, 0, 0] +# # target_pose = traj_l[10] +# traj_l = traj_l[::20] args = rf.config.get_sim_config("CURI") CURIsim = rf.sim.CURISim(args) diff --git a/rofunc/config/learning/rl/task/HumanoidHOTU.yaml b/rofunc/config/learning/rl/task/HumanoidHOTU.yaml new file mode 100644 index 000000000..7c7c68b30 --- /dev/null +++ b/rofunc/config/learning/rl/task/HumanoidHOTU.yaml @@ -0,0 +1,144 @@ +# used to create the object +name: HumanoidHOTU + +physics_engine: ${..physics_engine} + +# if given, will override the device setting in gym. +env: + numEnvs: ${resolve_default:4096,${...num_envs}} + envSpacing: 5 + episodeLength: 300 + cameraFollow: False # if the camera follows humanoid or not + isFlagrun: False + enableDebugVis: False + + pdControl: True + powerScale: 1.0 + controlFrequencyInv: 2 # 30 Hz + stateInit: "Hybrid" + hybridInitProb: 0.5 + numAMPObsSteps: 10 + recoveryEpisodeProb: 0.2 + recoverySteps: 60 + fallInitProb: 0.1 + + localRootObs: True + keyBodies: [ "right_hand", "left_hand", "right_foot", "left_foot" ] + contactBodies: ["right_foot", "left_foot"] + terminationHeight: 0.5 + enableEarlyTermination: True + + # animation files to learn from + motion_file: None + object_motion_file: None + + asset: + assetFileName: "mjcf/hotu_humanoid_w_qbhand_no_virtual.xml" + assetBodyNum: 15 + assetJointNum: 28 + + object_asset: + assetName: # List of object names, also used for finding objects in object_motion_file, if the name is box or sphere, it will be created as a box or sphere + assetFileName: # List of object asset file path + assetSize: # List of object size, used for scaling the object and creating the box objects + + plane: + staticFriction: 1.0 + dynamicFriction: 1.0 + restitution: 0.0 + +sim: + dt: 0.0166 # 1/60 s + substeps: 2 + up_axis: "z" + use_gpu_pipeline: ${eq:${...pipeline},"gpu"} + gravity: [0.0, 0.0, -9.81] + physx: + num_threads: ${....num_threads} + solver_type: ${....solver_type} + use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU + num_position_iterations: 4 + num_velocity_iterations: 0 + contact_offset: 0.02 + rest_offset: 0.0 + bounce_threshold_velocity: 0.2 + max_depenetration_velocity: 10.0 + default_buffer_size_multiplier: 10.0 + num_subscenes: ${....num_subscenes} + contact_collection: 2 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (default - all contacts) + + flex: + num_inner_iterations: 10 + warm_start: 0.25 + +task: + randomize: False + randomization_params: + # specify which attributes to randomize for each actor type and property + frequency: 600 # Define how many environment steps between generating new randomizations + observations: + range: [ 0, .002 ] # range for the white noise + operation: "additive" + distribution: "gaussian" + actions: + range: [ 0., .02 ] + operation: "additive" + distribution: "gaussian" + sim_params: + gravity: + range: [ 0, 0.4 ] + operation: "additive" + distribution: "gaussian" + schedule: "linear" # "linear" will linearly interpolate between no rand and max rand + schedule_steps: 3000 + actor_params: + humanoid: + color: True + rigid_body_properties: + mass: + range: [ 0.5, 1.5 ] + operation: "scaling" + distribution: "uniform" + setup_only: True # Property will only be randomized once before simulation is started. See Domain Randomization Documentation for more info. + schedule: "linear" # "linear" will linearly interpolate between no rand and max rand + schedule_steps: 3000 + rigid_shape_properties: + friction: + num_buckets: 500 + range: [ 0.7, 1.3 ] + operation: "scaling" + distribution: "uniform" + schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps` + schedule_steps: 3000 + restitution: + range: [ 0., 0.7 ] + operation: "scaling" + distribution: "uniform" + schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps` + schedule_steps: 3000 + dof_properties: + damping: + range: [ 0.5, 1.5 ] + operation: "scaling" + distribution: "uniform" + schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps` + schedule_steps: 3000 + stiffness: + range: [ 0.5, 1.5 ] + operation: "scaling" + distribution: "uniform" + schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps` + schedule_steps: 3000 + lower: + range: [ 0, 0.01 ] + operation: "additive" + distribution: "gaussian" + schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps` + schedule_steps: 3000 + upper: + range: [ 0, 0.01 ] + operation: "additive" + distribution: "gaussian" + schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps` + schedule_steps: 3000 + diff --git a/rofunc/config/learning/rl/task/HumanoidPhysHOI.yaml b/rofunc/config/learning/rl/task/HumanoidPhysHOI.yaml index 65d51027b..dab155d8f 100644 --- a/rofunc/config/learning/rl/task/HumanoidPhysHOI.yaml +++ b/rofunc/config/learning/rl/task/HumanoidPhysHOI.yaml @@ -1,8 +1,14 @@ -# if given, will override the device setting in gym. +# used to create the object +name: HumanoidPhysHOI + +physics_engine: ${..physics_engine} + +# if given, will override the device setting in gym. env: - numEnvs: 2048 + numEnvs: ${resolve_default:4096,${...num_envs}} envSpacing: 5 episodeLength: 40 + cameraFollow: False # if the camera follows humanoid or not isFlagrun: False enableDebugVis: False playdataset: False @@ -25,8 +31,10 @@ env: terminationHeight: 0.15 enableEarlyTermination: True + motion_file: + asset: - assetRoot: "physhoi/data/assets" +# assetRoot: "physhoi/data/assets" assetFileName: "smplx/smplx_capsule.xml" plane: @@ -51,7 +59,11 @@ env: cg2: 5. sim: + dt: 0.0166 # 1/60 s substeps: 2 + up_axis: "z" + use_gpu_pipeline: ${eq:${...pipeline},"gpu"} + gravity: [ 0.0, 0.0, -9.81 ] physx: num_threads: 4 solver_type: 1 # 0: pgs, 1: tgs diff --git a/rofunc/config/learning/rl/task/HumanoidPhysHOIViewMotion.yaml b/rofunc/config/learning/rl/task/HumanoidPhysHOIViewMotion.yaml new file mode 100644 index 000000000..f4b07c726 --- /dev/null +++ b/rofunc/config/learning/rl/task/HumanoidPhysHOIViewMotion.yaml @@ -0,0 +1,80 @@ +# used to create the object +name: HumanoidPhysHOIViewMotion + +physics_engine: ${..physics_engine} + +# if given, will override the device setting in gym. +env: + numEnvs: ${resolve_default:4096,${...num_envs}} + envSpacing: 5 + episodeLength: 40 + cameraFollow: False # if the camera follows humanoid or not + isFlagrun: False + enableDebugVis: False + playdataset: False + projtype: "None" + saveImages: False + initVel: False + + pdControl: True + powerScale: 1.0 + controlFrequencyInv: 2 # 30 Hz + stateInit: "Start" #Random + hybridInitProb: 0.5 + dataFPS: 25 + dataFramesScale: 1.2 # 25->30fps + ballSize: 1. + + localRootObs: False + keyBodies: ["Head", "L_Knee", "R_Knee", "L_Elbow", "R_Elbow", "L_Ankle", "R_Ankle", "L_Index3", "L_Middle3", "L_Pinky3", "L_Ring3","L_Thumb3","R_Index3", "R_Middle3", "R_Pinky3", "R_Ring3","R_Thumb3"] # #["L_Hip", "L_Knee", "left_foot", "L_Toe", "R_Hip", "R_Knee", "right_foot", "R_Toe", "Torso", "Spine", "Chest", "Neck", "Head", "L_Thorax", "L_Shoulder", "L_Elbow", "left_hand", "R_Thorax", "R_Shoulder", "R_Elbow", "right_hand"] #["right_hand", "left_hand", "right_foot", "left_foot"] + contactBodies: ["L_Index3", "L_Middle3", "L_Pinky3", "L_Ring3","L_Thumb3","R_Index3", "R_Middle3", "R_Pinky3", "R_Ring3","R_Thumb3"] #["right_foot", "left_foot"] + terminationHeight: 0.15 + enableEarlyTermination: True + + motion_file: + + asset: +# assetRoot: "physhoi/data/assets" + assetFileName: "smplx/smplx_capsule.xml" + + plane: + staticFriction: 1.0 + dynamicFriction: 1.0 + restitution: 1.6 + + rewardWeights: + p: 50. + r: 50. + pv: 0. + rv: 0. + + op: 1. + or: 0. + opv: 0. + orv: 0. + + ig: 20. + + cg1: 5. + cg2: 5. + +sim: + dt: 0.0166 # 1/60 s + substeps: 2 + up_axis: "z" + use_gpu_pipeline: ${eq:${...pipeline},"gpu"} + gravity: [ 0.0, 0.0, -9.81 ] + physx: + num_threads: 4 + solver_type: 1 # 0: pgs, 1: tgs + num_position_iterations: 4 + num_velocity_iterations: 0 + contact_offset: 0.02 + rest_offset: 0.0 + bounce_threshold_velocity: 0.2 + max_depenetration_velocity: 10.0 + default_buffer_size_multiplier: 10.0 + + flex: + num_inner_iterations: 10 + warm_start: 0.25 diff --git a/rofunc/config/learning/rl/task/humanoid_ase.yaml b/rofunc/config/learning/rl/task/humanoid_ase.yaml deleted file mode 100644 index b1cc03b39..000000000 --- a/rofunc/config/learning/rl/task/humanoid_ase.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# if given, will override the device setting in gym. -env: - numEnvs: 4096 - envSpacing: 5 - episodeLength: 300 - isFlagrun: False - enableDebugVis: False - - pdControl: True - powerScale: 1.0 - controlFrequencyInv: 2 # 30 Hz - stateInit: "Hybrid" - hybridInitProb: 0.5 - numAMPObsSteps: 10 - - localRootObs: True - keyBodies: ["right_hand", "left_hand", "right_foot", "left_foot"] - contactBodies: ["right_foot", "left_foot"] - terminationHeight: 0.15 - enableEarlyTermination: True - - asset: - assetRoot: "ase/data/assets" - assetFileName: "mjcf/amp_humanoid.xml" - - plane: - staticFriction: 1.0 - dynamicFriction: 1.0 - restitution: 0.0 - -sim: - substeps: 2 - physx: - num_threads: 4 - solver_type: 1 # 0: pgs, 1: tgs - num_position_iterations: 4 - num_velocity_iterations: 0 - contact_offset: 0.02 - rest_offset: 0.0 - bounce_threshold_velocity: 0.2 - max_depenetration_velocity: 10.0 - default_buffer_size_multiplier: 10.0 - - flex: - num_inner_iterations: 10 - warm_start: 0.25 diff --git a/rofunc/config/learning/rl/task/humanoid_ase_getup.yaml b/rofunc/config/learning/rl/task/humanoid_ase_getup.yaml deleted file mode 100644 index c6f040e18..000000000 --- a/rofunc/config/learning/rl/task/humanoid_ase_getup.yaml +++ /dev/null @@ -1,49 +0,0 @@ -# if given, will override the device setting in gym. -env: - numEnvs: 4096 - envSpacing: 5 - episodeLength: 300 - isFlagrun: False - enableDebugVis: False - - pdControl: True - powerScale: 1.0 - controlFrequencyInv: 2 # 30 Hz - stateInit: "Hybrid" - hybridInitProb: 0.5 - numAMPObsSteps: 10 - recoveryEpisodeProb: 0.2 - recoverySteps: 60 - fallInitProb: 0.1 - - localRootObs: True - keyBodies: ["right_hand", "left_hand", "right_foot", "left_foot"] - contactBodies: ["right_foot", "left_foot"] - terminationHeight: 0.15 - enableEarlyTermination: True - - asset: - assetRoot: "ase/data/assets" - assetFileName: "mjcf/amp_humanoid.xml" - - plane: - staticFriction: 1.0 - dynamicFriction: 1.0 - restitution: 0.0 - -sim: - substeps: 2 - physx: - num_threads: 4 - solver_type: 1 # 0: pgs, 1: tgs - num_position_iterations: 4 - num_velocity_iterations: 0 - contact_offset: 0.02 - rest_offset: 0.0 - bounce_threshold_velocity: 0.2 - max_depenetration_velocity: 10.0 - default_buffer_size_multiplier: 10.0 - - flex: - num_inner_iterations: 10 - warm_start: 0.25 diff --git a/rofunc/config/learning/rl/task/humanoid_ase_sword_shield.yaml b/rofunc/config/learning/rl/task/humanoid_ase_sword_shield.yaml deleted file mode 100644 index 49e1d410a..000000000 --- a/rofunc/config/learning/rl/task/humanoid_ase_sword_shield.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# if given, will override the device setting in gym. -env: - numEnvs: 4096 - envSpacing: 5 - episodeLength: 300 - isFlagrun: False - enableDebugVis: False - - pdControl: True - powerScale: 1.0 - controlFrequencyInv: 2 # 30 Hz - stateInit: "Hybrid" - hybridInitProb: 0.5 - numAMPObsSteps: 10 - - localRootObs: True - keyBodies: ["right_hand", "left_hand", "right_foot", "left_foot", "sword", "shield"] - contactBodies: ["right_foot", "left_foot"] - terminationHeight: 0.15 - enableEarlyTermination: True - - asset: - assetRoot: "ase/data/assets" - assetFileName: "mjcf/amp_humanoid_sword_shield.xml" - - plane: - staticFriction: 1.0 - dynamicFriction: 1.0 - restitution: 0.0 - -sim: - substeps: 2 - physx: - num_threads: 4 - solver_type: 1 # 0: pgs, 1: tgs - num_position_iterations: 4 - num_velocity_iterations: 0 - contact_offset: 0.02 - rest_offset: 0.0 - bounce_threshold_velocity: 0.2 - max_depenetration_velocity: 10.0 - default_buffer_size_multiplier: 10.0 - - flex: - num_inner_iterations: 10 - warm_start: 0.25 diff --git a/rofunc/config/learning/rl/task/humanoid_ase_sword_shield_getup.yaml b/rofunc/config/learning/rl/task/humanoid_ase_sword_shield_getup.yaml deleted file mode 100644 index 5c40201fa..000000000 --- a/rofunc/config/learning/rl/task/humanoid_ase_sword_shield_getup.yaml +++ /dev/null @@ -1,53 +0,0 @@ -# if given, will override the device setting in gym. -env: - numEnvs: 4096 - envSpacing: 5 - episodeLength: 300 - isFlagrun: False - enableDebugVis: False - - pdControl: True - powerScale: 1.0 - controlFrequencyInv: 2 # 30 Hz - stateInit: "Hybrid" - hybridInitProb: 0.5 - numAMPObsSteps: 10 - recoveryEpisodeProb: 0.2 - recoverySteps: 60 - fallInitProb: 0.1 - - localRootObs: True - keyBodies: ["right_hand", "left_hand", "right_foot", "left_foot", "sword", "shield"] - contactBodies: ["right_foot", "left_foot"] - terminationHeight: 0.15 - enableEarlyTermination: True - - # animation files to learn from - motion_file: None - - asset: - assetRoot: "ase/data/assets" - assetFileName: "mjcf/amp_humanoid_sword_shield.xml" - - plane: - staticFriction: 1.0 - dynamicFriction: 1.0 - restitution: 0.0 - -sim: - substeps: 2 - up_axis: "z" - physx: - num_threads: 4 - solver_type: 1 # 0: pgs, 1: tgs - num_position_iterations: 4 - num_velocity_iterations: 0 - contact_offset: 0.02 - rest_offset: 0.0 - bounce_threshold_velocity: 0.2 - max_depenetration_velocity: 10.0 - default_buffer_size_multiplier: 10.0 - - flex: - num_inner_iterations: 10 - warm_start: 0.25 diff --git a/rofunc/config/learning/rl/task/humanoid_ase_sword_shield_getup_test.yaml b/rofunc/config/learning/rl/task/humanoid_ase_sword_shield_getup_test.yaml deleted file mode 100644 index ebdb5ddea..000000000 --- a/rofunc/config/learning/rl/task/humanoid_ase_sword_shield_getup_test.yaml +++ /dev/null @@ -1,49 +0,0 @@ -# if given, will override the device setting in gym. -env: - numEnvs: 4096 - envSpacing: 5 - episodeLength: 100 - isFlagrun: False - enableDebugVis: False - - pdControl: True - powerScale: 1.0 - controlFrequencyInv: 2 # 30 Hz - stateInit: "Default" - hybridInitProb: 0.5 - numAMPObsSteps: 10 - recoveryEpisodeProb: 0.2 - recoverySteps: 60 - fallInitProb: 1 - - localRootObs: True - keyBodies: ["right_hand", "left_hand", "right_foot", "left_foot", "sword", "shield"] - contactBodies: ["right_foot", "left_foot"] - terminationHeight: 0.15 - enableEarlyTermination: True - - asset: - assetRoot: "ase/data/assets" - assetFileName: "mjcf/amp_humanoid_sword_shield.xml" - - plane: - staticFriction: 1.0 - dynamicFriction: 1.0 - restitution: 0.0 - -sim: - substeps: 2 - physx: - num_threads: 4 - solver_type: 1 # 0: pgs, 1: tgs - num_position_iterations: 4 - num_velocity_iterations: 0 - contact_offset: 0.02 - rest_offset: 0.0 - bounce_threshold_velocity: 0.2 - max_depenetration_velocity: 10.0 - default_buffer_size_multiplier: 10.0 - - flex: - num_inner_iterations: 10 - warm_start: 0.25 diff --git a/rofunc/config/learning/rl/task/humanoid_ase_sword_shield_test.yaml b/rofunc/config/learning/rl/task/humanoid_ase_sword_shield_test.yaml deleted file mode 100644 index db5e4892f..000000000 --- a/rofunc/config/learning/rl/task/humanoid_ase_sword_shield_test.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# if given, will override the device setting in gym. -env: - numEnvs: 4096 - envSpacing: 5 - episodeLength: 300 - isFlagrun: False - enableDebugVis: False - - pdControl: True - powerScale: 1.0 - controlFrequencyInv: 2 # 30 Hz - stateInit: "Default" - hybridInitProb: 0.5 - numAMPObsSteps: 10 - - localRootObs: True - keyBodies: ["right_hand", "left_hand", "right_foot", "left_foot", "sword", "shield"] - contactBodies: ["right_foot", "left_foot"] - terminationHeight: 0.15 - enableEarlyTermination: True - - asset: - assetRoot: "ase/data/assets" - assetFileName: "mjcf/amp_humanoid_sword_shield.xml" - - plane: - staticFriction: 1.0 - dynamicFriction: 1.0 - restitution: 0.0 - -sim: - substeps: 2 - physx: - num_threads: 4 - solver_type: 1 # 0: pgs, 1: tgs - num_position_iterations: 4 - num_velocity_iterations: 0 - contact_offset: 0.02 - rest_offset: 0.0 - bounce_threshold_velocity: 0.2 - max_depenetration_velocity: 10.0 - default_buffer_size_multiplier: 10.0 - - flex: - num_inner_iterations: 10 - warm_start: 0.25 diff --git a/rofunc/config/learning/rl/task/humanoid_sword_shield.yaml b/rofunc/config/learning/rl/task/humanoid_sword_shield.yaml deleted file mode 100644 index 99f1fc3fc..000000000 --- a/rofunc/config/learning/rl/task/humanoid_sword_shield.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# if given, will override the device setting in gym. -env: - numEnvs: 4096 - envSpacing: 5 - episodeLength: 300 - isFlagrun: False - enableDebugVis: False - - pdControl: True - powerScale: 1.0 - controlFrequencyInv: 2 # 30 Hz - stateInit: "Random" - hybridInitProb: 0.5 - numAMPObsSteps: 10 - - localRootObs: False - keyBodies: ["right_hand", "left_hand", "right_foot", "left_foot", "sword", "shield"] - contactBodies: ["right_foot", "left_foot"] - terminationHeight: 0.15 - enableEarlyTermination: True - - asset: - assetRoot: "ase/data/assets" - assetFileName: "mjcf/amp_humanoid_sword_shield.xml" - - plane: - staticFriction: 1.0 - dynamicFriction: 1.0 - restitution: 0.0 - -sim: - substeps: 2 - physx: - num_threads: 4 - solver_type: 1 # 0: pgs, 1: tgs - num_position_iterations: 4 - num_velocity_iterations: 0 - contact_offset: 0.02 - rest_offset: 0.0 - bounce_threshold_velocity: 0.2 - max_depenetration_velocity: 10.0 - default_buffer_size_multiplier: 10.0 - - flex: - num_inner_iterations: 10 - warm_start: 0.25 diff --git a/rofunc/config/learning/rl/task/humanoid_sword_shield_heading.yaml b/rofunc/config/learning/rl/task/humanoid_sword_shield_heading.yaml deleted file mode 100644 index 20c1b35ec..000000000 --- a/rofunc/config/learning/rl/task/humanoid_sword_shield_heading.yaml +++ /dev/null @@ -1,53 +0,0 @@ -# if given, will override the device setting in gym. -env: - numEnvs: 4096 - envSpacing: 5 - episodeLength: 300 - isFlagrun: False - enableDebugVis: False - - pdControl: True - powerScale: 1.0 - controlFrequencyInv: 2 # 30 Hz - stateInit: "Default" - hybridInitProb: 0.5 - numAMPObsSteps: 10 - - localRootObs: True - keyBodies: ["right_hand", "left_hand", "right_foot", "left_foot", "sword", "shield"] - contactBodies: ["right_foot", "left_foot"] - terminationHeight: 0.15 - enableEarlyTermination: True - - tarSpeedMin: 1.5 - tarSpeedMax: 1.6 - headingChangeStepsMin: 100 - headingChangeStepsMax: 200 - enableRandHeading: True - enableTaskObs: True - - asset: - assetRoot: "ase/data/assets" - assetFileName: "mjcf/amp_humanoid_sword_shield.xml" - - plane: - staticFriction: 1.0 - dynamicFriction: 1.0 - restitution: 0.0 - -sim: - substeps: 2 - physx: - num_threads: 4 - solver_type: 1 # 0: pgs, 1: tgs - num_position_iterations: 4 - num_velocity_iterations: 0 - contact_offset: 0.02 - rest_offset: 0.0 - bounce_threshold_velocity: 0.2 - max_depenetration_velocity: 10.0 - default_buffer_size_multiplier: 10.0 - - flex: - num_inner_iterations: 10 - warm_start: 0.25 diff --git a/rofunc/config/learning/rl/task/humanoid_sword_shield_location.yaml b/rofunc/config/learning/rl/task/humanoid_sword_shield_location.yaml deleted file mode 100644 index 31d05f882..000000000 --- a/rofunc/config/learning/rl/task/humanoid_sword_shield_location.yaml +++ /dev/null @@ -1,52 +0,0 @@ -# if given, will override the device setting in gym. -env: - numEnvs: 4096 - envSpacing: 5 - episodeLength: 300 - isFlagrun: False - enableDebugVis: False - - pdControl: True - powerScale: 1.0 - controlFrequencyInv: 2 # 30 Hz - stateInit: "Default" - hybridInitProb: 0.5 - numAMPObsSteps: 10 - - localRootObs: True - keyBodies: ["right_hand", "left_hand", "right_foot", "left_foot", "sword", "shield"] - contactBodies: ["right_foot", "left_foot"] - terminationHeight: 0.15 - enableEarlyTermination: True - - tarSpeed: 1.0 - tarChangeStepsMin: 100 - tarChangeStepsMax: 200 - tarDistMax: 10.0 - enableTaskObs: True - - asset: - assetRoot: "ase/data/assets" - assetFileName: "mjcf/amp_humanoid_sword_shield.xml" - - plane: - staticFriction: 1.0 - dynamicFriction: 1.0 - restitution: 0.0 - -sim: - substeps: 2 - physx: - num_threads: 4 - solver_type: 1 # 0: pgs, 1: tgs - num_position_iterations: 4 - num_velocity_iterations: 0 - contact_offset: 0.02 - rest_offset: 0.0 - bounce_threshold_velocity: 0.2 - max_depenetration_velocity: 10.0 - default_buffer_size_multiplier: 10.0 - - flex: - num_inner_iterations: 10 - warm_start: 0.25 diff --git a/rofunc/config/learning/rl/task/humanoid_sword_shield_reach.yaml b/rofunc/config/learning/rl/task/humanoid_sword_shield_reach.yaml deleted file mode 100644 index 48a57417e..000000000 --- a/rofunc/config/learning/rl/task/humanoid_sword_shield_reach.yaml +++ /dev/null @@ -1,56 +0,0 @@ -# if given, will override the device setting in gym. -env: - numEnvs: 4096 - envSpacing: 5 - episodeLength: 300 - isFlagrun: False - enableDebugVis: False - - pdControl: True - powerScale: 1.0 - controlFrequencyInv: 2 # 30 Hz - stateInit: "Default" - hybridInitProb: 0.5 - numAMPObsSteps: 10 - - localRootObs: True - keyBodies: ["right_hand", "left_hand", "right_foot", "left_foot", "sword", "shield"] - contactBodies: ["right_foot", "left_foot"] - terminationHeight: 0.15 - enableEarlyTermination: True - - tarSpeed: 1.0 - tarChangeStepsMin: 50 - tarChangeStepsMax: 100 - tarDistMax: 1.0 - tarHeightMin: 0.2 - tarHeightMax: 2.0 - - reachBodyName: "sword" - enableTaskObs: True - - asset: - assetRoot: "ase/data/assets" - assetFileName: "mjcf/amp_humanoid_sword_shield.xml" - - plane: - staticFriction: 1.0 - dynamicFriction: 1.0 - restitution: 0.0 - -sim: - substeps: 2 - physx: - num_threads: 4 - solver_type: 1 # 0: pgs, 1: tgs - num_position_iterations: 4 - num_velocity_iterations: 0 - contact_offset: 0.02 - rest_offset: 0.0 - bounce_threshold_velocity: 0.2 - max_depenetration_velocity: 10.0 - default_buffer_size_multiplier: 10.0 - - flex: - num_inner_iterations: 10 - warm_start: 0.25 diff --git a/rofunc/config/learning/rl/task/humanoid_sword_shield_strike.yaml b/rofunc/config/learning/rl/task/humanoid_sword_shield_strike.yaml deleted file mode 100644 index 9f0bbafc1..000000000 --- a/rofunc/config/learning/rl/task/humanoid_sword_shield_strike.yaml +++ /dev/null @@ -1,49 +0,0 @@ -# if given, will override the device setting in gym. -env: - numEnvs: 4096 - envSpacing: 5 - episodeLength: 300 - isFlagrun: False - enableDebugVis: False - - pdControl: True - powerScale: 1.0 - controlFrequencyInv: 2 # 30 Hz - stateInit: "Default" - hybridInitProb: 0.5 - numAMPObsSteps: 10 - - localRootObs: True - keyBodies: ["right_hand", "left_hand", "right_foot", "left_foot", "sword", "shield"] - contactBodies: ["right_foot", "left_foot"] - terminationHeight: 0.15 - enableEarlyTermination: True - - strikeBodyNames: ["sword", "right_hand", "right_lower_arm"] - enableTaskObs: True - - asset: - assetRoot: "ase/data/assets" - assetFileName: "mjcf/amp_humanoid_sword_shield.xml" - - plane: - staticFriction: 1.0 - dynamicFriction: 1.0 - restitution: 0.0 - -sim: - substeps: 2 - physx: - num_threads: 4 - solver_type: 1 # 0: pgs, 1: tgs - num_position_iterations: 4 - num_velocity_iterations: 0 - contact_offset: 0.02 - rest_offset: 0.0 - bounce_threshold_velocity: 0.2 - max_depenetration_velocity: 10.0 - default_buffer_size_multiplier: 10.0 - - flex: - num_inner_iterations: 10 - warm_start: 0.25 diff --git a/rofunc/config/learning/rl/train/BaseTaskAMPRofuncRL.yaml b/rofunc/config/learning/rl/train/BaseTaskAMPRofuncRL.yaml index 3f7fd27a7..1953177a8 100644 --- a/rofunc/config/learning/rl/train/BaseTaskAMPRofuncRL.yaml +++ b/rofunc/config/learning/rl/train/BaseTaskAMPRofuncRL.yaml @@ -3,7 +3,7 @@ Trainer: experiment_name: # Experiment name for logging. experiment_directory: # Experiment directory for logging. write_interval: 100 # TensorBoard write interval for logging. (timesteps) - checkpoint_interval: # Checkpoint interval for logging. (timesteps) + checkpoint_interval: 10000 # Checkpoint interval for logging. (timesteps) wandb: True # If true, log to Weights & Biases. wandb_kwargs: # Weights & Biases kwargs. https://docs.wandb.ai/ref/python/init project: RofuncRL # Weights & Biases project name. diff --git a/rofunc/config/learning/rl/train/BaseTaskPHYSHOIRofuncRL.yaml b/rofunc/config/learning/rl/train/BaseTaskPHYSHOIRofuncRL.yaml new file mode 100644 index 000000000..f8a1a2c20 --- /dev/null +++ b/rofunc/config/learning/rl/train/BaseTaskPHYSHOIRofuncRL.yaml @@ -0,0 +1,93 @@ +# ========== Trainer parameters ========== +Trainer: + experiment_name: # Experiment name for logging. + experiment_directory: # Experiment directory for logging. + write_interval: 100 # TensorBoard write interval for logging. (timesteps) + checkpoint_interval: # Checkpoint interval for logging. (timesteps) + wandb: False # If true, log to Weights & Biases. + wandb_kwargs: # Weights & Biases kwargs. https://docs.wandb.ai/ref/python/init + project: RofuncRL # Weights & Biases project name. + name: ${..experiment_name} # Weights & Biases run name. + rofunc_logger_kwargs: # Rofunc BeautyLogger kwargs. + verbose: True # If true, print to stdout. + maximum_steps: 100000 # The maximum number of steps to run for. + random_steps: 0 # The number of random exploration steps to take. + start_learning_steps: 0 # The number of steps to take before starting network updating. + seed: 42 # The random seed. + rollouts: 16 # The number of rollouts before updating. + max_episode_steps: 200 # The maximum number of steps per episode. + eval_flag: False # If true, run evaluation. + eval_freq: ${multi:${.max_episode_steps},5} # The frequency of evaluation. (timesteps) + eval_steps: 1000 # The number of steps to run for evaluation. + use_eval_thread: False # If true, use a separate thread for evaluation. + inference_steps: 1000 # The number of steps to run for inference. + + +# ========== Agent parameters ========== +Agent: + discount: 0.99 # The discount factor, gamma. + td_lambda: 0.95 # TD(lambda) coefficient (lam) for computing returns and advantages. + + learning_epochs: 8 # The number of epochs to train for per update. + mini_batch_size: 8 # The number of samples per update. + + lr_a: 2e-5 # Learning rate for actor. + lr_c: 2e-5 # Learning rate for critic. +# lr_scheduler: # Learning rate scheduler type. +# lr_scheduler_kwargs: # Learning rate scheduler kwargs. + adam_eps: 1e-5 # Adam epsilon. + + # If true, use the Generalized Advantage Estimator (GAE) + # with a value function, see https://arxiv.org/pdf/1506.02438.pdf. + use_gae: True + + entropy_loss_scale: 0.0 # entropy loss scaling factor + value_loss_scale: 5.0 # value loss scaling factor + + grad_norm_clip: 1.0 # clipping coefficient for the norm of the gradients + ratio_clip: 0.2 # clipping coefficient for computing the clipped surrogate objective + value_clip: 0.2 # clipping coefficient for computing the value loss (if clip_predicted_values is True) + clip_predicted_values: True # clip predicted values during value loss computation + + kl_threshold: 0 # Initial coefficient for KL divergence. + + action_deterministic: False # If true, use the mean action for deterministic policy. + +# state_preprocessor: # State preprocessor type. +# state_preprocessor_kwargs: # State preprocessor kwargs. +# value_preprocessor: # Value preprocessor type. +# value_preprocessor_kwargs: # Value preprocessor kwargs. +# rewards_shaper: # Rewards shaper type. + + +# ========= Model parameters ========== +Model: + use_init: True + use_action_clip: True # If true, clip actions to the action space range. + use_same_model: True # If true, use the same model for the actor and critic. + use_action_out_tanh: False # If true, use tanh to scale the action outputs to the action space range. + action_clip: 1.0 # clipping coefficient for the norm of the actions + action_scale: 1.0 # scaling coefficient for the actions + use_log_std_clip: True # If true, clip log standard deviations to the range [-20, 2]. + log_std_clip_max: 2.0 # clipping coefficient for the log standard deviations + log_std_clip_min: -20 # clipping coefficient for the log standard deviations + + actor: + type: "Gaussian" # ["Beta", "Gaussian"] + mlp_hidden_dims: [ 1024, 512 ] + mlp_activation: relu + use_lstm: false + lstm_cell_size: 256 + lstm_use_prev_action: false + lstm_use_prev_reward: false + max_seq_len: 20 + + critic: + mlp_hidden_dims: [ 1024, 512 ] + mlp_activation: relu + use_lstm: false + lstm_cell_size: 256 + lstm_use_prev_action: false + lstm_use_prev_reward: false + max_seq_len: 20 + diff --git a/rofunc/config/learning/rl/train/BaseTaskPhysHOI.yaml b/rofunc/config/learning/rl/train/BaseTaskPhysHOI.yaml deleted file mode 100644 index 59a93fba1..000000000 --- a/rofunc/config/learning/rl/train/BaseTaskPhysHOI.yaml +++ /dev/null @@ -1,72 +0,0 @@ -params: - seed: -1 - - algo: - name: physhoi - - model: - name: physhoi - - network: - name: physhoi - separate: True - - space: - continuous: - mu_activation: None - sigma_activation: None - mu_init: - name: default - sigma_init: - name: const_initializer - val: -2.9 - fixed_sigma: True - learn_sigma: False - - mlp: - units: [1024, 512] - activation: relu - d2rl: False - - initializer: - name: default - regularizer: - name: None - - # load_checkpoint: True - load_checkpoint: False - - config: - name: PhysHOI - env_name: rlgpu - multi_gpu: False - ppo: True - mixed_precision: False - normalize_input: True - normalize_value: True - reward_shaper: - scale_value: 1 - normalize_advantage: True - gamma: 0.99 - tau: 0.95 - learning_rate: 2e-5 - lr_schedule: constant - score_to_win: 20000 - max_epochs: 50000 - save_best_after: 50 - save_frequency: 50 - print_stats: True - grad_norm: 1.0 - entropy_coef: 0.0 - truncate_grads: False - ppo: True - e_clip: 0.2 - horizon_length: 32 - minibatch_size: 16384 - mini_epochs: 6 - critic_coef: 5 - clip_value: False - seq_len: 4 - bounds_loss_coef: 10 - enable_eps_greedy: False - resume_from: None \ No newline at end of file diff --git a/rofunc/config/learning/rl/train/HumanoidASEGetupSwordShieldASERofuncRL.yaml b/rofunc/config/learning/rl/train/HumanoidASEGetupSwordShieldASERofuncRL.yaml index 588cae84b..acc0a8b2b 100644 --- a/rofunc/config/learning/rl/train/HumanoidASEGetupSwordShieldASERofuncRL.yaml +++ b/rofunc/config/learning/rl/train/HumanoidASEGetupSwordShieldASERofuncRL.yaml @@ -3,7 +3,7 @@ Trainer: experiment_name: # Experiment name for logging. experiment_directory: # Experiment directory for logging. write_interval: 100 # TensorBoard write interval for logging. (timesteps) - checkpoint_interval: # Checkpoint interval for logging. (timesteps) + checkpoint_interval: 10000 # Checkpoint interval for logging. (timesteps) wandb: True # If true, log to Weights & Biases. wandb_kwargs: # Weights & Biases kwargs. https://docs.wandb.ai/ref/python/init project: RofuncRL # Weights & Biases project name. diff --git a/rofunc/config/learning/rl/train/HumanoidASEHeadingSwordShieldASERofuncRL.yaml b/rofunc/config/learning/rl/train/HumanoidASEHeadingSwordShieldASERofuncRL.yaml index 132505534..a013d2be2 100644 --- a/rofunc/config/learning/rl/train/HumanoidASEHeadingSwordShieldASERofuncRL.yaml +++ b/rofunc/config/learning/rl/train/HumanoidASEHeadingSwordShieldASERofuncRL.yaml @@ -3,7 +3,7 @@ Trainer: experiment_name: # Experiment name for logging. experiment_directory: # Experiment directory for logging. write_interval: 100 # TensorBoard write interval for logging. (timesteps) - checkpoint_interval: # Checkpoint interval for logging. (timesteps) + checkpoint_interval: 10000 # Checkpoint interval for logging. (timesteps) wandb: True # If true, log to Weights & Biases. wandb_kwargs: # Weights & Biases kwargs. https://docs.wandb.ai/ref/python/init project: RofuncRL # Weights & Biases project name. diff --git a/rofunc/config/learning/rl/train/HumanoidASELocationSwordShieldASERofuncRL.yaml b/rofunc/config/learning/rl/train/HumanoidASELocationSwordShieldASERofuncRL.yaml index 132505534..a013d2be2 100644 --- a/rofunc/config/learning/rl/train/HumanoidASELocationSwordShieldASERofuncRL.yaml +++ b/rofunc/config/learning/rl/train/HumanoidASELocationSwordShieldASERofuncRL.yaml @@ -3,7 +3,7 @@ Trainer: experiment_name: # Experiment name for logging. experiment_directory: # Experiment directory for logging. write_interval: 100 # TensorBoard write interval for logging. (timesteps) - checkpoint_interval: # Checkpoint interval for logging. (timesteps) + checkpoint_interval: 10000 # Checkpoint interval for logging. (timesteps) wandb: True # If true, log to Weights & Biases. wandb_kwargs: # Weights & Biases kwargs. https://docs.wandb.ai/ref/python/init project: RofuncRL # Weights & Biases project name. diff --git a/rofunc/config/learning/rl/train/HumanoidASEPerturbSwordShieldASERofuncRL.yaml b/rofunc/config/learning/rl/train/HumanoidASEPerturbSwordShieldASERofuncRL.yaml index 588cae84b..acc0a8b2b 100644 --- a/rofunc/config/learning/rl/train/HumanoidASEPerturbSwordShieldASERofuncRL.yaml +++ b/rofunc/config/learning/rl/train/HumanoidASEPerturbSwordShieldASERofuncRL.yaml @@ -3,7 +3,7 @@ Trainer: experiment_name: # Experiment name for logging. experiment_directory: # Experiment directory for logging. write_interval: 100 # TensorBoard write interval for logging. (timesteps) - checkpoint_interval: # Checkpoint interval for logging. (timesteps) + checkpoint_interval: 10000 # Checkpoint interval for logging. (timesteps) wandb: True # If true, log to Weights & Biases. wandb_kwargs: # Weights & Biases kwargs. https://docs.wandb.ai/ref/python/init project: RofuncRL # Weights & Biases project name. diff --git a/rofunc/config/learning/rl/train/HumanoidASEReachSwordShieldASERofuncRL.yaml b/rofunc/config/learning/rl/train/HumanoidASEReachSwordShieldASERofuncRL.yaml index 676fc32ba..874087fc5 100644 --- a/rofunc/config/learning/rl/train/HumanoidASEReachSwordShieldASERofuncRL.yaml +++ b/rofunc/config/learning/rl/train/HumanoidASEReachSwordShieldASERofuncRL.yaml @@ -3,7 +3,7 @@ Trainer: experiment_name: # Experiment name for logging. experiment_directory: # Experiment directory for logging. write_interval: 100 # TensorBoard write interval for logging. (timesteps) - checkpoint_interval: # Checkpoint interval for logging. (timesteps) + checkpoint_interval: 10000 # Checkpoint interval for logging. (timesteps) wandb: True # If true, log to Weights & Biases. wandb_kwargs: # Weights & Biases kwargs. https://docs.wandb.ai/ref/python/init project: RofuncRL # Weights & Biases project name. diff --git a/rofunc/config/learning/rl/train/HumanoidASEStrikeSwordShieldASERofuncRL.yaml b/rofunc/config/learning/rl/train/HumanoidASEStrikeSwordShieldASERofuncRL.yaml index 676fc32ba..874087fc5 100644 --- a/rofunc/config/learning/rl/train/HumanoidASEStrikeSwordShieldASERofuncRL.yaml +++ b/rofunc/config/learning/rl/train/HumanoidASEStrikeSwordShieldASERofuncRL.yaml @@ -3,7 +3,7 @@ Trainer: experiment_name: # Experiment name for logging. experiment_directory: # Experiment directory for logging. write_interval: 100 # TensorBoard write interval for logging. (timesteps) - checkpoint_interval: # Checkpoint interval for logging. (timesteps) + checkpoint_interval: 10000 # Checkpoint interval for logging. (timesteps) wandb: True # If true, log to Weights & Biases. wandb_kwargs: # Weights & Biases kwargs. https://docs.wandb.ai/ref/python/init project: RofuncRL # Weights & Biases project name. diff --git a/rofunc/config/learning/rl/train/HumanoidHOTURofuncRL.yaml b/rofunc/config/learning/rl/train/HumanoidHOTURofuncRL.yaml new file mode 100644 index 000000000..45ae3f68b --- /dev/null +++ b/rofunc/config/learning/rl/train/HumanoidHOTURofuncRL.yaml @@ -0,0 +1,126 @@ +# ========== Trainer parameters ========== +Trainer: + experiment_name: # Experiment name for logging. + experiment_directory: # Experiment directory for logging. + write_interval: 100 # TensorBoard write interval for logging. (timesteps) + checkpoint_interval: 10000 # Checkpoint interval for logging. (timesteps) + wandb: True # If true, log to Weights & Biases. + wandb_kwargs: # Weights & Biases kwargs. https://docs.wandb.ai/ref/python/init + project: RofuncRL # Weights & Biases project name. + name: ${..experiment_name} # Weights & Biases run name. + rofunc_logger_kwargs: # Rofunc BeautyLogger kwargs. + verbose: True # If true, print to stdout. + maximum_steps: 100000 # The maximum number of steps to run for. + random_steps: 0 # The number of random exploration steps to take. + start_learning_steps: 0 # The number of steps to take before starting network updating. + seed: 42 # The random seed. + rollouts: 32 # The number of rollouts before updating. + max_episode_steps: 200 # The maximum number of steps per episode. + eval_flag: False # If true, run evaluation. + eval_freq: ${multi:${.max_episode_steps},5} # The frequency of evaluation. (timesteps) + eval_steps: 1000 # The number of steps to run for evaluation. + use_eval_thread: False # If true, use a separate thread for evaluation. + inference_steps: 1000 # The number of steps to run for inference. + + +# ========== Agent parameters ========== +Agent: + discount: 0.99 # The discount factor, gamma. + td_lambda: 0.95 # TD(lambda) coefficient (lam) for computing returns and advantages. + + learning_epochs: 6 # The number of epochs to train for per update. + mini_batch_size: 8 # The number of samples per update. + amp_batch_size: 512 # The number of samples per update. + + lr_a: 5e-5 # Learning rate for actor. + lr_c: 5e-5 # Learning rate for critic. + lr_d: 5e-5 # Learning rate for discriminator. + lr_e: 5e-5 # Learning rate for encoder. +# lr_scheduler: # Learning rate scheduler type. +# lr_scheduler_kwargs: # Learning rate scheduler kwargs. + adam_eps: 1e-5 # Adam epsilon. + + entropy_loss_scale: 0.0 # entropy loss scaling factor + value_loss_scale: 2.5 # value loss scaling factor + + grad_norm_clip: 0.0 # clipping coefficient for the norm of the gradients + ratio_clip: 0.2 # clipping coefficient for computing the clipped surrogate objective + value_clip: 0.2 # clipping coefficient for computing the value loss (if clip_predicted_values is True) + clip_predicted_values: false # clip predicted values during value loss computation + + least_square_discriminator: false + discriminator_loss_scale: 5.0 + discriminator_batch_size: 4096 + discriminator_logit_regularization_scale: 0.01 + discriminator_gradient_penalty_scale: 5 + discriminator_weight_decay_scale: 0.0001 + + ase_latent_dim: 64 + ase_latent_steps_min: 1 + ase_latent_steps_max: 150 + + enc_reward_scale: 1 + enc_weight_decay_scale: 0 + enc_gradient_penalty_scale: 0 + + task_reward_weight: 0.0 + style_reward_weight: 2.0 + enc_reward_weight: 0.5 + discriminator_reward_scale: 2 + + kl_threshold: 0 # Initial coefficient for KL divergence. + +# state_preprocessor: # State preprocessor type. +# state_preprocessor_kwargs: # State preprocessor kwargs. +# value_preprocessor: # Value preprocessor type. +# value_preprocessor_kwargs: # Value preprocessor kwargs. +# rewards_shaper: # Rewards shaper type. + + +# ========= Model parameters ========== +Model: + use_init: True + use_action_clip: False # If true, clip actions to the action space range. + use_action_out_tanh: True # If true, apply tanh to the output of the actor. + action_clip: 1.0 # clipping coefficient for the norm of the actions + action_scale: 1.0 # scaling coefficient for the actions + use_log_std_clip: True # If true, clip log standard deviations to the range [-20, 2]. + log_std_clip_max: 2.0 # clipping coefficient for the log standard deviations + log_std_clip_min: -20 # clipping coefficient for the log standard deviations + + actor: + type: "Gaussian" # ["Beta", "Gaussian"] + mlp_hidden_dims: [ 1024, 512 ] + mlp_activation: relu + use_lstm: false + lstm_cell_size: 256 + lstm_use_prev_action: false + lstm_use_prev_reward: false + max_seq_len: 20 + + critic: + mlp_hidden_dims: [ 1024, 512 ] + mlp_activation: relu + use_lstm: false + lstm_cell_size: 256 + lstm_use_prev_action: false + lstm_use_prev_reward: false + max_seq_len: 20 + + discriminator: + mlp_hidden_dims: [ 1024, 512 ] + mlp_activation: relu + use_lstm: false + lstm_cell_size: 256 + lstm_use_prev_action: false + lstm_use_prev_reward: false + max_seq_len: 20 + + encoder: + mlp_hidden_dims: [ 1024, 512 ] + mlp_activation: relu + use_lstm: false + lstm_cell_size: 256 + lstm_use_prev_action: false + lstm_use_prev_reward: false + max_seq_len: 20 diff --git a/rofunc/config/simulator/CURI.yaml b/rofunc/config/simulator/CURI.yaml index a47013907..3168135ef 100644 --- a/rofunc/config/simulator/CURI.yaml +++ b/rofunc/config/simulator/CURI.yaml @@ -14,8 +14,8 @@ env: robot_name: "CURI" assetRoot: # assetFile: "urdf/curi/urdf/curi_isaacgym_dual_arm.urdf" - assetFile: "urdf/curi/urdf/curi_isaacgym_dual_arm_w_softhand.urdf" -# assetFile: "urdf/curi/urdf/curi_w_softhand_isaacgym.urdf" +# assetFile: "urdf/curi/urdf/curi_isaacgym_dual_arm_w_softhand.urdf" + assetFile: "urdf/curi/urdf/curi_w_softhand_isaacgym.urdf" init_pose: [1.3, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0] fix_base_link: False disable_gravity: False diff --git a/rofunc/learning/RofuncRL/agents/mixline/amp_agent.py b/rofunc/learning/RofuncRL/agents/mixline/amp_agent.py index 8b5b0d954..395f60468 100644 --- a/rofunc/learning/RofuncRL/agents/mixline/amp_agent.py +++ b/rofunc/learning/RofuncRL/agents/mixline/amp_agent.py @@ -17,11 +17,12 @@ import gym import gymnasium -import rofunc as rf import torch import torch.nn as nn import torch.nn.functional as F from omegaconf import DictConfig + +import rofunc as rf from rofunc.learning.RofuncRL.agents.base_agent import BaseAgent from rofunc.learning.RofuncRL.models.actor_models import ActorAMP from rofunc.learning.RofuncRL.models.critic_models import Critic @@ -195,8 +196,9 @@ def _set_up(self): self.replay_buffer.create_tensor(name="states", size=self.amp_observation_space, dtype=torch.float32) # initialize motion dataset - for _ in range(math.ceil(self.motion_dataset.memory_size / self._amp_batch_size)): - self.motion_dataset.add_samples(states=self.collect_reference_motions(self._amp_batch_size)) + if self.collect_reference_motions is not None: + for _ in range(math.ceil(self.motion_dataset.memory_size / self._amp_batch_size)): + self.motion_dataset.add_samples(states=self.collect_reference_motions(self._amp_batch_size)) def act(self, states: torch.Tensor, deterministic: bool = False): if self._current_states is not None: @@ -354,10 +356,10 @@ def update_net(self): # discriminator prediction loss if self._least_square_discriminator: discriminator_loss = 0.5 * ( - F.mse_loss(amp_cat_logits, -torch.ones_like(amp_cat_logits), reduction='mean') \ + F.mse_loss(amp_cat_logits, -torch.ones_like(amp_cat_logits), reduction='mean') + F.mse_loss(amp_motion_logits, torch.ones_like(amp_motion_logits), reduction='mean')) else: - discriminator_loss = 0.5 * (nn.BCEWithLogitsLoss()(amp_cat_logits, torch.zeros_like(amp_cat_logits)) \ + discriminator_loss = 0.5 * (nn.BCEWithLogitsLoss()(amp_cat_logits, torch.zeros_like(amp_cat_logits)) + nn.BCEWithLogitsLoss()(amp_motion_logits, torch.ones_like(amp_motion_logits))) @@ -380,7 +382,7 @@ def update_net(self): # discriminator weight decay if self._discriminator_weight_decay_scale: - weights = [torch.flatten(module.weight) for module in self.discriminator.modules() \ + weights = [torch.flatten(module.weight) for module in self.discriminator.modules() if isinstance(module, torch.nn.Linear)] weight_decay = torch.sum(torch.square(torch.cat(weights, dim=-1))) discriminator_loss += self._discriminator_weight_decay_scale * weight_decay diff --git a/rofunc/learning/RofuncRL/agents/mixline/hotu_agent.py b/rofunc/learning/RofuncRL/agents/mixline/hotu_agent.py new file mode 100644 index 000000000..74ebe0b8f --- /dev/null +++ b/rofunc/learning/RofuncRL/agents/mixline/hotu_agent.py @@ -0,0 +1,77 @@ +# Copyright 2023, Junjia LIU, jjliu@mae.cuhk.edu.hk +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import Callable, Union, Tuple, Optional, List + +import gym +import gymnasium +import torch +import torch.nn as nn +import torch.nn.functional as F +from omegaconf import DictConfig + +import rofunc as rf +from rofunc.learning.RofuncRL.agents.mixline.ase_agent import ASEAgent +from rofunc.learning.RofuncRL.models.actor_models import ActorAMP +from rofunc.learning.RofuncRL.models.critic_models import Critic +from rofunc.learning.RofuncRL.processors.schedulers import KLAdaptiveRL +from rofunc.learning.RofuncRL.processors.standard_scaler import RunningStandardScaler +from rofunc.learning.RofuncRL.processors.standard_scaler import empty_preprocessor +from rofunc.learning.RofuncRL.utils.memory import Memory + + +class HOTUAgent(ASEAgent): + """ + Adversarial Motion Priors (AMP) agent \n + “AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control”. Peng et al. 2021. https://arxiv.org/abs/2104.02180 \n + Rofunc documentation: https://rofunc.readthedocs.io/en/latest/lfd/RofuncRL/AMP.html + """ + + def __init__(self, + cfg: DictConfig, + observation_space: Optional[Union[int, Tuple[int], gym.Space, gymnasium.Space, List]], + action_space: Optional[Union[int, Tuple[int], gym.Space, gymnasium.Space]], + memory: Optional[Union[Memory, Tuple[Memory]]] = None, + device: Optional[Union[str, torch.device]] = None, + experiment_dir: Optional[str] = None, + rofunc_logger: Optional[rf.logger.BeautyLogger] = None, + amp_observation_space: Optional[Union[int, Tuple[int], gym.Space, gymnasium.Space]] = None, + motion_dataset: Optional[Union[Memory, Tuple[Memory]]] = None, + replay_buffer: Optional[Union[Memory, Tuple[Memory]]] = None, + collect_reference_motions: Optional[Callable[[int], torch.Tensor]] = None): + """ + :param cfg: Configuration + :param observation_space: Observation space + :param action_space: Action space + :param memory: Memory for storing transitions + :param device: Device on which the torch tensor is allocated + :param experiment_dir: Directory where experiment outputs are saved + :param rofunc_logger: Rofunc logger + :param amp_observation_space: cfg["env"]["numAMPObsSteps"] * NUM_AMP_OBS_PER_STEP + :param motion_dataset: Motion dataset + :param replay_buffer: Replay buffer + :param collect_reference_motions: Function for collecting reference motions + """ + super().__init__(cfg=cfg, + observation_space=observation_space, + action_space=action_space, + memory=memory, + device=device, + experiment_dir=experiment_dir, + rofunc_logger=rofunc_logger, + amp_observation_space=amp_observation_space, + motion_dataset=motion_dataset, + replay_buffer=replay_buffer, + collect_reference_motions=collect_reference_motions) diff --git a/rofunc/learning/RofuncRL/agents/mixline/physhoi_agent.py b/rofunc/learning/RofuncRL/agents/mixline/physhoi_agent.py index a3a7274f5..8dd30c978 100644 --- a/rofunc/learning/RofuncRL/agents/mixline/physhoi_agent.py +++ b/rofunc/learning/RofuncRL/agents/mixline/physhoi_agent.py @@ -12,21 +12,251 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import Union, Tuple, Optional + import gym import gymnasium +import rofunc as rf import torch import torch.nn as nn import torch.nn.functional as F from omegaconf import DictConfig -from typing import Callable, Union, Tuple, Optional - -import rofunc as rf from rofunc.learning.RofuncRL.agents.base_agent import BaseAgent -from rofunc.learning.RofuncRL.agents.mixline.amp_agent import AMPAgent -from rofunc.learning.RofuncRL.models.base_models import BaseMLP +from rofunc.learning.RofuncRL.agents.online.ppo_agent import PPOAgent +from rofunc.learning.RofuncRL.processors.schedulers import KLAdaptiveRL from rofunc.learning.RofuncRL.utils.memory import Memory -class PhysHOIAgent(AMPAgent): - def __init__(self): - super().__init__() +class PhysHOIAgent(PPOAgent, BaseAgent): + """ + PhysHOI agent + """ + + def __init__(self, + cfg: DictConfig, + observation_space: Optional[Union[int, Tuple[int], gym.Space, gymnasium.Space]], + action_space: Optional[Union[int, Tuple[int], gym.Space, gymnasium.Space]], + memory: Optional[Union[Memory, Tuple[Memory]]] = None, + device: Optional[Union[str, torch.device]] = None, + experiment_dir: Optional[str] = None, + rofunc_logger: Optional[rf.logger.BeautyLogger] = None): + """ + :param cfg: Configuration + :param observation_space: Observation space + :param action_space: Action space + :param memory: Memory for storing transitions + :param device: Device on which the torch tensor is allocated + :param experiment_dir: Directory where experiment outputs are saved + :param rofunc_logger: Rofunc logger + """ + super().__init__( + cfg, + observation_space, + action_space, + memory, + device, + experiment_dir, + rofunc_logger, + ) + if hasattr(cfg.Model, "state_encoder"): + img_channel = int(self.cfg.Model.state_encoder.inp_channels) + img_size = int(self.cfg.Model.state_encoder.image_size) + state_tensor_size = (img_channel, img_size, img_size) + kd = True + else: + state_tensor_size = self.observation_space + kd = False + self.memory.create_tensor(name="next_states", size=state_tensor_size, dtype=torch.float32, keep_dimensions=kd) + self.memory.create_tensor(name="next_values", size=1, dtype=torch.float32) + + self._rewards_shaper = self.cfg.get("Agent", {}).get("rewards_shaper", lambda rewards: rewards * 1) + + self.scaler = torch.cuda.amp.GradScaler(enabled=False) + + def store_transition(self, states: torch.Tensor, actions: torch.Tensor, next_states: torch.Tensor, + rewards: torch.Tensor, terminated: torch.Tensor, truncated: torch.Tensor, infos: torch.Tensor): + BaseAgent.store_transition(self, states=states, actions=actions, next_states=next_states, rewards=rewards, + terminated=terminated, truncated=truncated, infos=infos) + self._current_next_states = next_states + + # reward shaping + if self._rewards_shaper is not None: + rewards = self._rewards_shaper(rewards) + + # compute values + if self.cfg.Model.use_same_model: + values = self.value.get_value(self._state_preprocessor(states)) + else: + values = self.value(self._state_preprocessor(states)) + values = self._value_preprocessor(values, inverse=True) + + if self.cfg.Model.use_same_model: + next_values = self.value.get_value(self._state_preprocessor(next_states)) + else: + next_values = self.value(self._state_preprocessor(next_states)) + next_values = self._value_preprocessor(next_values, inverse=True) + next_values *= infos['terminate'].view(-1, 1).logical_not() + + # storage transition in memory + self.memory.add_samples(states=states, actions=actions, rewards=rewards, next_states=next_states, + terminated=terminated, truncated=truncated, log_prob=self._current_log_prob, + values=values, next_values=next_values) + + def bound_loss(self, mu): + soft_bound = 1.0 + mu_loss_high = torch.clamp_min(mu - soft_bound, 0.0) ** 2 + mu_loss_low = torch.clamp_max(mu + soft_bound, 0.0) ** 2 + b_loss = (mu_loss_low + mu_loss_high).sum(axis=-1) + + return b_loss + + def update_net(self): + """ + Update the network + """ + '''Compute Generalized Advantage Estimator (GAE)''' + values = self.memory.get_tensor_by_name("values") + next_values = self.memory.get_tensor_by_name("next_values") + + advantage = 0 + advantages = torch.zeros_like(self.memory.get_tensor_by_name("rewards")) + not_dones = self.memory.get_tensor_by_name("terminated").logical_not() + memory_size = self.memory.get_tensor_by_name("rewards").shape[0] + + # advantages computation + for i in reversed(range(memory_size)): + advantage = self.memory.get_tensor_by_name("rewards")[i] - values[i] + self._discount * ( + next_values[i] + self._td_lambda * not_dones[i] * advantage) + advantages[i] = advantage + # returns computation + values_target = advantages + values + # advantage normalization + advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8) + + self.memory.set_tensor_by_name("values", self._value_preprocessor(values, train=True)) + self.memory.set_tensor_by_name("returns", self._value_preprocessor(values_target, train=True)) + self.memory.set_tensor_by_name("advantages", advantages) + + '''Sample mini-batches from memory and update the network''' + sampled_batches = self.memory.sample_all(names=self._tensors_names, mini_batches=self._mini_batch_size) + + cumulative_policy_loss = 0 + cumulative_entropy_loss = 0 + cumulative_value_loss = 0 + + # learning epochs + for epoch in range(self._learning_epochs): + kl_divergences = [] + + # mini-batches loop + for i, ( + sampled_states, sampled_actions, sampled_dones, sampled_log_prob, sampled_values, + sampled_returns, + sampled_advantages) in enumerate(sampled_batches): + sampled_states = self._state_preprocessor(sampled_states, train=not epoch) + _, log_prob_now = self.policy(sampled_states, sampled_actions) + + # compute approximate KL divergence + with torch.no_grad(): + ratio = log_prob_now - sampled_log_prob + kl_divergence = ((torch.exp(ratio) - 1) - ratio).mean() + kl_divergences.append(kl_divergence) + + # early stopping with KL divergence + if self._kl_threshold and kl_divergence > self._kl_threshold: + break + + # compute entropy loss + entropy_loss = -self._entropy_loss_scale * self.policy.get_entropy().mean() + + # compute policy loss + ratio = torch.exp(log_prob_now - sampled_log_prob) + surrogate = sampled_advantages * ratio + surrogate_clipped = sampled_advantages * torch.clip(ratio, 1.0 - self._ratio_clip, + 1.0 + self._ratio_clip) + + policy_loss = -torch.min(surrogate, surrogate_clipped).mean() + + # compute value loss + if self.cfg.Model.use_same_model: + predicted_values = self.value.get_value(sampled_states) + else: + predicted_values = self.value(sampled_states) + + if self._clip_predicted_values: + predicted_values = sampled_values + torch.clip(predicted_values - sampled_values, + min=-self._value_clip, + max=self._value_clip) + value_loss = self._value_loss_scale * F.mse_loss(sampled_returns, predicted_values) + + # compute bound loss + b_loss = 10 * self.bound_loss(sampled_actions) + b_loss = torch.mean(b_loss) + + loss = policy_loss + value_loss + b_loss + + self.optimizer.zero_grad() + self.scaler.scale(loss).backward() + self.scaler.unscale_(self.optimizer) + if self._grad_norm_clip > 0: + nn.utils.clip_grad_norm_(self.policy.parameters(), self._grad_norm_clip) + self.scaler.step(self.optimizer) + self.scaler.update() + + # if self.policy is self.value: + # # optimization step + # self.optimizer.zero_grad() + # (policy_loss + entropy_loss + value_loss + b_loss).backward() + # if self._grad_norm_clip > 0: + # nn.utils.clip_grad_norm_(self.policy.parameters(), self._grad_norm_clip) + # self.optimizer.step() + # else: + # # Update policy network + # self.optimizer_policy.zero_grad() + # (policy_loss + entropy_loss + b_loss).backward() + # if self._grad_norm_clip > 0: + # nn.utils.clip_grad_norm_(self.policy.parameters(), self._grad_norm_clip) + # self.optimizer_policy.step() + # + # # Update value network + # self.optimizer_value.zero_grad() + # value_loss.backward() + # if self._grad_norm_clip > 0: + # nn.utils.clip_grad_norm_(self.value.parameters(), self._grad_norm_clip) + # self.optimizer_value.step() + + # update cumulative losses + cumulative_policy_loss += policy_loss.item() + cumulative_value_loss += value_loss.item() + if self._entropy_loss_scale: + cumulative_entropy_loss += entropy_loss.item() + + # # update learning rate + # if self._lr_scheduler: + # if self.policy is self.value: + # if isinstance(self.scheduler, KLAdaptiveRL): + # self.scheduler.step(torch.tensor(kl_divergences).mean()) + # else: + # self.scheduler.step() + # else: + # if isinstance(self.scheduler_policy, KLAdaptiveRL): + # self.scheduler_policy.step(torch.tensor(kl_divergences).mean()) + # else: + # self.scheduler_policy.step() + # if isinstance(self.scheduler_value, KLAdaptiveRL): + # self.scheduler_value.step(torch.tensor(kl_divergences).mean()) + # else: + # self.scheduler_value.step() + + # record data + self.track_data("Loss / Policy loss", cumulative_policy_loss / (self._learning_epochs * self._mini_batch_size)) + self.track_data("Loss / Value loss", cumulative_value_loss / (self._learning_epochs * self._mini_batch_size)) + if self._entropy_loss_scale: + self.track_data("Loss / Entropy loss", + cumulative_entropy_loss / (self._learning_epochs * self._mini_batch_size)) + if self._lr_scheduler: + if self.policy is self.value: + self.track_data("Learning / Learning rate", self.scheduler.get_last_lr()[0]) + else: + self.track_data("Learning / Learning rate (policy)", self.scheduler_policy.get_last_lr()[0]) + self.track_data("Learning / Learning rate (value)", self.scheduler_value.get_last_lr()[0]) diff --git a/rofunc/learning/RofuncRL/agents/online/ppo_agent.py b/rofunc/learning/RofuncRL/agents/online/ppo_agent.py index 20ba7aaed..2fc44b36c 100644 --- a/rofunc/learning/RofuncRL/agents/online/ppo_agent.py +++ b/rofunc/learning/RofuncRL/agents/online/ppo_agent.py @@ -114,9 +114,9 @@ def __init__(self, self._kl_threshold = self.cfg.Agent.kl_threshold self._rewards_shaper = self.cfg.get("Agent", {}).get("rewards_shaper", lambda rewards: rewards * 0.01) # self._state_preprocessor = None # TODO: Check - # self._state_preprocessor = RunningStandardScaler - # self._state_preprocessor_kwargs = self.cfg.get("Agent", {}).get("state_preprocessor_kwargs", - # {"size": observation_space, "device": device}) + self._state_preprocessor = RunningStandardScaler + self._state_preprocessor_kwargs = self.cfg.get("Agent", {}).get("state_preprocessor_kwargs", + {"size": observation_space, "device": device}) # self._state_preprocessor = Normalization # self._state_preprocessor_kwargs = self.cfg.get("Agent", {}).get("state_preprocessor_kwargs", # {"shape": observation_space, "device": device}) @@ -172,9 +172,9 @@ def act(self, states: torch.Tensor, deterministic: bool = False): actions = self.policy.mean(self._state_preprocessor(states)).detach() log_prob = None else: - actions, _ = self.policy(self._state_preprocessor(states)) - actions = actions.detach() - log_prob = None + actions, log_prob = self.policy(self._state_preprocessor(states), deterministic=True) + # actions = actions.detach() + self._current_log_prob = log_prob return actions, log_prob def store_transition(self, states: torch.Tensor, actions: torch.Tensor, next_states: torch.Tensor, diff --git a/rofunc/learning/RofuncRL/models/actor_models.py b/rofunc/learning/RofuncRL/models/actor_models.py index f514eec93..0589d08ff 100644 --- a/rofunc/learning/RofuncRL/models/actor_models.py +++ b/rofunc/learning/RofuncRL/models/actor_models.py @@ -163,7 +163,7 @@ def forward(self, state, action=None, deterministic=False): else: output_action = self.cfg.action_scale * self.mean_layer(state) - log_prob = None + log_prob = torch.zeros(output_action.shape[0], 1, device=output_action.device) if not deterministic: log_std = self.log_std if self.cfg.use_log_std_clip: diff --git a/rofunc/learning/RofuncRL/tasks/__init__.py b/rofunc/learning/RofuncRL/tasks/__init__.py index 512860674..58a4b859c 100644 --- a/rofunc/learning/RofuncRL/tasks/__init__.py +++ b/rofunc/learning/RofuncRL/tasks/__init__.py @@ -19,6 +19,9 @@ def __init__(self, env_type="isaacgym"): from .isaacgymenv.ase.humanoid_reach import HumanoidReachTask from .isaacgymenv.ase.humanoid_strike import HumanoidStrikeTask from .isaacgymenv.ase.humanoid_view_motion import HumanoidASEViewMotionTask + from .isaacgymenv.physhoi.humanoid_physhoi import HumanoidPhysHOITask + # from .isaacgymenv.physhoi.physhoi import PhysHOI_BallPlay + from .isaacgymenv.hotu.humanoid_hotu import HumanoidHOTUTask from .isaacgymenv.hotu.humanoid_view_motion import HumanoidHOTUViewMotionTask from .isaacgymenv.hands.shadow_hand_block_stack import ShadowHandBlockStackTask from .isaacgymenv.hands.shadow_hand_bottle_cap import ShadowHandBottleCapTask @@ -65,6 +68,9 @@ def __init__(self, env_type="isaacgym"): "HumanoidASEReachSwordShield": HumanoidReachTask, "HumanoidASEStrikeSwordShield": HumanoidStrikeTask, "HumanoidASEViewMotion": HumanoidASEViewMotionTask, + "HumanoidPhysHOI": HumanoidPhysHOITask, + # "HumanoidPhysHOI": PhysHOI_BallPlay, + "HumanoidHOTU": HumanoidHOTUTask, "HumanoidHOTUViewMotion": HumanoidHOTUViewMotionTask, "BiShadowHandOver": ShadowHandOverTask, "BiShadowHandBlockStack": ShadowHandBlockStackTask, diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/ase/humanoid.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/ase/humanoid.py index f0de92dac..f9b759c0e 100644 --- a/rofunc/learning/RofuncRL/tasks/isaacgymenv/ase/humanoid.py +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/ase/humanoid.py @@ -99,7 +99,7 @@ def __init__(self, config, rl_device, sim_device, graphics_device_id, headless, num_actors = self.get_num_actors_per_env() self._humanoid_root_states = self._root_states.view(self.num_envs, num_actors, actor_root_state.shape[-1])[..., - 0, :] + 0, :] self._initial_humanoid_root_states = self._humanoid_root_states.clone() self._initial_humanoid_root_states[:, 7:13] = 0 @@ -159,14 +159,11 @@ def create_sim(self, **kwargs): if self.randomize: self.apply_randomizations(self.randomization_params) - return - def reset_idx(self, env_ids): self._reset_actors(env_ids) self._reset_env_tensors(env_ids) self._refresh_sim_tensors() self._compute_observations(env_ids) - return def _reset_actors(self, env_ids): self._humanoid_root_states[env_ids] = self._initial_humanoid_root_states[env_ids] @@ -632,7 +629,7 @@ def _update_debug_viz(self): ##################################################################### -###=========================jit functions=========================### +### =========================jit functions=========================### ##################################################################### diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid.py index cab5f3b01..df4c16938 100644 --- a/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid.py +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid.py @@ -37,7 +37,6 @@ def __init__(self, config, rl_device, sim_device, graphics_device_id, headless, self.power_scale = self.cfg["env"]["powerScale"] self.randomize = self.cfg["task"]["randomize"] self.debug_viz = self.cfg["env"]["enableDebugVis"] - self.camera_follow = self.cfg["env"].get("cameraFollow", False) self.plane_static_friction = self.cfg["env"]["plane"]["staticFriction"] self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"] self.plane_restitution = self.cfg["env"]["plane"]["restitution"] @@ -47,6 +46,8 @@ def __init__(self, config, rl_device, sim_device, graphics_device_id, headless, self._contact_bodies = self.cfg["env"]["contactBodies"] self._termination_height = self.cfg["env"]["terminationHeight"] self._enable_early_termination = self.cfg["env"]["enableEarlyTermination"] + self.camera_follow = self.cfg["env"].get("cameraFollow", False) + key_bodies = self.cfg["env"]["keyBodies"] self._setup_character_props(key_bodies) @@ -362,7 +363,7 @@ def _create_envs(self, spacing, num_per_row): asset_options.angular_damping = 0.01 asset_options.max_angular_velocity = 100.0 asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE - asset_options.disable_gravity = True + asset_options.disable_gravity = False # asset_options.fix_base_link = True humanoid_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options) actuator_props = self.gym.get_asset_actuator_properties(humanoid_asset) @@ -387,13 +388,19 @@ def _create_envs(self, spacing, num_per_row): asset_options.angular_damping = 0.01 asset_options.max_angular_velocity = 100.0 asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE + asset_options.use_mesh_materials = True + asset_options.mesh_normal_mode = gymapi.COMPUTE_PER_VERTEX + asset_options.vhacd_enabled = True + asset_options.vhacd_params = gymapi.VhacdParams() + asset_options.vhacd_params.max_num_vertices_per_ch = 8 + asset_options.vhacd_params.resolution = 500000 # asset_options.disable_gravity = True # asset_options.fix_base_link = True - if 'box' in self.object_names[i].lower(): + if 'box' in self.object_names[i].lower() and object_asset_files[i] is None: object_size = self.cfg["env"]["object_asset"]["assetSize"][i] object_asset = self.gym.create_box(self.sim, *object_size, asset_options) - elif self.object_names[i].lower() == 'sphere': + elif 'sphere' in self.object_names[i].lower() and object_asset_files[i] is None: object_radius = self.cfg["env"]["object_asset"]["assetSize"][i] object_asset = self.gym.create_sphere(self.sim, object_radius, asset_options) else: @@ -485,7 +492,8 @@ def _add_object(self, env_id, env_ptr, object_name, object_asset): start_pose.p = gymapi.Vec3(*get_axis_params(char_h, self.up_axis_idx)) start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0) - col_group = 100000 if object_name is 'base' else env_id + # col_group = 100000 if object_name is 'base' else env_id + col_group = 100000 object_handle = self.gym.create_actor( env_ptr, @@ -639,7 +647,6 @@ def render(self, sync_frame_time=False): self._update_camera() super().render(sync_frame_time) - return def _build_key_body_ids_tensor(self, key_body_names): env_ptr = self.envs[0] @@ -722,7 +729,6 @@ def _update_camera(self): self.gym.viewer_camera_look_at(self.viewer, None, new_cam_pos, new_cam_target) self._cam_prev_char_pos[:] = char_root_pos - return def _update_debug_viz(self): self.gym.clear_lines(self.viewer) diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid_hotu.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid_hotu.py index 0cee1c297..48bc33bf0 100644 --- a/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid_hotu.py +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid_hotu.py @@ -24,7 +24,7 @@ from rofunc.learning.RofuncRL.tasks.utils import torch_jit_utils as torch_utils -class HumanoidHOTU(Humanoid): +class HumanoidHOTUTask(Humanoid): class StateInit(Enum): Default = 0 Start = 1 @@ -35,7 +35,7 @@ def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, vir self.cfg = cfg state_init = cfg["env"]["stateInit"] - self._state_init = HumanoidHOTU.StateInit[state_init] + self._state_init = HumanoidHOTUTask.StateInit[state_init] self._hybrid_init_prob = cfg["env"]["hybridInitProb"] self._num_amp_obs_steps = cfg["env"]["numAMPObsSteps"] assert self._num_amp_obs_steps >= 2 @@ -182,7 +182,7 @@ def _load_object_motion(self, object_motion_file): object_motion_file=object_motion_file, object_names=self.cfg["env"]["object_asset"]["assetName"], device=self.device, - height_offset=0 + height_offset=self._motion_lib.humanoid_height_offsets[0] # TODO: make it for multiple motions ) def reset_idx(self, env_ids): @@ -193,14 +193,14 @@ def reset_idx(self, env_ids): self._init_amp_obs(env_ids) def _reset_actors(self, env_ids): - if self._state_init == HumanoidHOTU.StateInit.Default: + if self._state_init == HumanoidHOTUTask.StateInit.Default: self._reset_default(env_ids) elif ( - self._state_init == HumanoidHOTU.StateInit.Start - or self._state_init == HumanoidHOTU.StateInit.Random + self._state_init == HumanoidHOTUTask.StateInit.Start + or self._state_init == HumanoidHOTUTask.StateInit.Random ): self._reset_ref_state_init(env_ids) - elif self._state_init == HumanoidHOTU.StateInit.Hybrid: + elif self._state_init == HumanoidHOTUTask.StateInit.Hybrid: self._reset_hybrid_state_init(env_ids) else: assert False, "Unsupported state initialization strategy: {:s}".format( @@ -220,11 +220,11 @@ def _reset_ref_state_init(self, env_ids): motion_ids = self._motion_lib.sample_motions(num_envs) if ( - self._state_init == HumanoidHOTU.StateInit.Random - or self._state_init == HumanoidHOTU.StateInit.Hybrid + self._state_init == HumanoidHOTUTask.StateInit.Random + or self._state_init == HumanoidHOTUTask.StateInit.Hybrid ): motion_times = self._motion_lib.sample_time(motion_ids) - elif self._state_init == HumanoidHOTU.StateInit.Start: + elif self._state_init == HumanoidHOTUTask.StateInit.Start: motion_times = torch.zeros(num_envs, device=self.device) else: assert ( @@ -239,8 +239,8 @@ def _reset_ref_state_init(self, env_ids): root_ang_vel, dof_vel, key_pos, + _, _ ) = self._motion_lib.get_motion_state(motion_ids, motion_times) - self._set_env_state( env_ids=env_ids, root_pos=root_pos, @@ -308,6 +308,7 @@ def _init_amp_obs_ref(self, env_ids, motion_ids, motion_times): root_ang_vel, dof_vel, key_pos, + _, _ ) = self._motion_lib.get_motion_state(motion_ids, motion_times) amp_obs_demo = build_amp_observations( root_pos, diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid_view_motion.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid_view_motion.py index ecf7a3ed7..14ef14880 100644 --- a/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid_view_motion.py +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/humanoid_view_motion.py @@ -16,10 +16,10 @@ from isaacgym import gymtorch import rofunc as rf -from rofunc.learning.RofuncRL.tasks.isaacgymenv.hotu.humanoid_hotu import HumanoidHOTU +from rofunc.learning.RofuncRL.tasks.isaacgymenv.hotu.humanoid_hotu import HumanoidHOTUTask -class HumanoidHOTUViewMotionTask(HumanoidHOTU): +class HumanoidHOTUViewMotionTask(HumanoidHOTUTask): def __init__( self, cfg, diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/motion_lib.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/motion_lib.py index 1fbdef5c6..d5545edf1 100644 --- a/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/motion_lib.py +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/motion_lib.py @@ -100,6 +100,14 @@ def __init__(self, motion_file, dof_body_ids, dof_offsets, key_body_ids, device) self._load_motions(motion_file) motions = self._motions + + self.humanoid_height_offsets = [] + for motion in motions: + tar_global_pos = motion.global_translation + min_h = torch.min(tar_global_pos[..., 2]) + motion.global_translation[..., 2] -= min_h + self.humanoid_height_offsets.append(min_h) + self.gts = torch.cat([m.global_translation for m in motions], dim=0).float() self.grs = torch.cat([m.global_rotation for m in motions], dim=0).float() self.lrs = torch.cat([m.local_rotation for m in motions], dim=0).float() @@ -109,6 +117,8 @@ def __init__(self, motion_file, dof_body_ids, dof_offsets, key_body_ids, device) ).float() self.dvs = torch.cat([m.dof_vels for m in motions], dim=0).float() + + lengths = self._motion_num_frames lengths_shifted = lengths.roll(1) lengths_shifted[0] = 0 @@ -516,8 +526,8 @@ def _load_motions(self): object_poses_dict = {} for object_name in self.object_names: data_ptr = labels.index(f'{object_name}.pose.x') - # assert data_ptr + 6 == labels.index(f'{object_name}.pose.qw') - pose = data[:, data_ptr:data_ptr + 3] + assert data_ptr + 6 == labels.index(f'{object_name}.pose.qw') + pose = data[:, data_ptr:data_ptr + 7] pose[:, :3] *= self.scales[i] # convert to meter pose = self._motion_transform(torch.tensor(pose, dtype=torch.float)) @@ -596,16 +606,17 @@ def _motion_transform(self, pose): new_position[:, :3] = raw_position.resize(num_samples, 3, 1) new_position = torch.bmm(homo_matrix.expand(num_samples, 4, 4), new_position) - new_pose = torch.zeros((num_samples, 7)) - new_pose[:, 3:] = torch.tensor([0, 0, 0, 1], dtype=torch.float32) + new_pose = torch.zeros((num_samples, 7)) + # new_pose[:, 3:] = torch.tensor([0, 0, 0, 1], dtype=torch.float32) new_pose[:, :3] = new_position[:, :3].resize(num_samples, 3) - + new_pose[:, 2] -= self.height_offset.cpu() # pose[:, :3] = new_position[:, :3].resize(num_samples, 3) - # pose[:, 3:] = rf.robolab.quaternion_multiply_tensor_multirow2(torch.tensor([0.5, 0.5, 0.5, 0.5]), - # torch.tensor(pose[:, 3:])) - # pose[:, 3:] = rf.robolab.quaternion_multiply_tensor_multirow2(torch.tensor([0.5, 0.5, 0.5, 0.5]), - # torch.tensor(pose[:, 3:])) + new_pose[:, 3:] = rf.robolab.quaternion_multiply_tensor_multirow2(torch.tensor([0.5, 0.5, 0.5, 0.5]), + torch.tensor(pose[:, 3:])) + # new_pose[:, 3:] = rf.robolab.quaternion_multiply_tensor_multirow2(torch.tensor([0, 0, -0.383, 0.924]), + new_pose[:, 3:] = rf.robolab.quaternion_multiply_tensor_multirow2(torch.tensor([0, 0, 0.924, 0.383]), + torch.tensor(new_pose[:, 3:])) return new_pose diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/tmp.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/tmp.py new file mode 100644 index 000000000..c309e05b7 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hotu/tmp.py @@ -0,0 +1,1385 @@ +# Copyright 2023, Junjia LIU, jjliu@mae.cuhk.edu.hk +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from enum import Enum + +import rofunc as rf +from gym import spaces +from isaacgym import gymapi +from isaacgym import gymtorch +from isaacgym.torch_utils import * +from rofunc.learning.RofuncRL.tasks.isaacgymenv.base.vec_task import VecTask +from rofunc.learning.RofuncRL.tasks.isaacgymenv.hotu.humanoid import dof_to_obs +from rofunc.learning.RofuncRL.tasks.isaacgymenv.hotu.motion_lib import MotionLib, ObjectMotionLib +from rofunc.learning.RofuncRL.tasks.utils import torch_jit_utils as torch_utils +from rofunc.utils.oslab.path import get_rofunc_path + + +class Humanoid(VecTask): + """ + This class is a wrapper of the Isaac Gym environment for the Humanoid task. + """ + + def __init__(self, config, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, + force_render): + # Load the config + self.cfg = config + self._pd_control = self.cfg["env"]["pdControl"] + self.power_scale = self.cfg["env"]["powerScale"] + self.randomize = self.cfg["task"]["randomize"] + self.debug_viz = self.cfg["env"]["enableDebugVis"] + self.plane_static_friction = self.cfg["env"]["plane"]["staticFriction"] + self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"] + self.plane_restitution = self.cfg["env"]["plane"]["restitution"] + self.max_episode_length = self.cfg["env"]["episodeLength"] + self._local_root_obs = self.cfg["env"]["localRootObs"] + self._root_height_obs = self.cfg["env"].get("rootHeightObs", True) + self._contact_bodies = self.cfg["env"]["contactBodies"] + self._termination_height = self.cfg["env"]["terminationHeight"] + self._enable_early_termination = self.cfg["env"]["enableEarlyTermination"] + self.camera_follow = self.cfg["env"].get("cameraFollow", False) + + key_bodies = self.cfg["env"]["keyBodies"] + self._setup_character_props(key_bodies) + + # Set the dimensions of the observation and action spaces + self.cfg["env"]["numObservations"] = self.get_obs_size() + self.cfg["env"]["numActions"] = self.get_action_size() + + super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, + graphics_device_id=graphics_device_id, headless=headless, + virtual_screen_capture=virtual_screen_capture, force_render=force_render) + + dt = self.cfg["sim"]["dt"] + self.dt = self.control_freq_inv * dt + + # Acquiring the state tensors from the simulator + actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim) + dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim) + sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim) + rigid_body_state = self.gym.acquire_rigid_body_state_tensor(self.sim) + contact_force_tensor = self.gym.acquire_net_contact_force_tensor(self.sim) + + sensors_per_env = 2 + self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, sensors_per_env * 6) + + dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim) + self.dof_force_tensor = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs, self.num_dof) + + # Update state tensor buffers + self.gym.refresh_dof_state_tensor(self.sim) + self.gym.refresh_actor_root_state_tensor(self.sim) + self.gym.refresh_rigid_body_state_tensor(self.sim) + self.gym.refresh_net_contact_force_tensor(self.sim) + + self._root_states = gymtorch.wrap_tensor(actor_root_state) + num_actors = self.get_num_actors_per_env() + + self._humanoid_root_states = self._root_states.view(self.num_envs, num_actors, actor_root_state.shape[-1])[..., + 0, :] + self._initial_humanoid_root_states = self._humanoid_root_states.clone() + self._initial_humanoid_root_states[:, 7:13] = 0 + + # Get the actor ids for the humanoid and the objects + self._humanoid_actor_ids = num_actors * torch.arange(self.num_envs, device=self.device, dtype=torch.int32) + if self.object_names is not None: + self._object_actor_ids = { + object_name: torch.tensor( + [self.gym.get_actor_index(self.envs[i], self.object_handles[object_name][i], gymapi.DOMAIN_SIM) for + i in range(self.num_envs)], dtype=torch.int32, device=self.device) for object_name in + self.object_names} + + # create some wrapper tensors for different slices + self._dof_state = gymtorch.wrap_tensor(dof_state_tensor) + dofs_per_env = self._dof_state.shape[0] // self.num_envs + self._dof_pos = self._dof_state.view(self.num_envs, dofs_per_env, 2)[..., :self.num_dof, 0] + self._dof_vel = self._dof_state.view(self.num_envs, dofs_per_env, 2)[..., :self.num_dof, 1] + + self._initial_dof_pos = torch.zeros_like(self._dof_pos, device=self.device, dtype=torch.float) + self._initial_dof_vel = torch.zeros_like(self._dof_vel, device=self.device, dtype=torch.float) + + self._rigid_body_state = gymtorch.wrap_tensor(rigid_body_state) + bodies_per_env = self._rigid_body_state.shape[0] // self.num_envs + rigid_body_state_reshaped = self._rigid_body_state.view(self.num_envs, bodies_per_env, 13) + + self._rigid_body_pos = rigid_body_state_reshaped[..., :self.num_bodies, 0:3] + self._rigid_body_rot = rigid_body_state_reshaped[..., :self.num_bodies, 3:7] + self._rigid_body_vel = rigid_body_state_reshaped[..., :self.num_bodies, 7:10] + self._rigid_body_ang_vel = rigid_body_state_reshaped[..., :self.num_bodies, 10:13] + + contact_force_tensor = gymtorch.wrap_tensor(contact_force_tensor) + self._contact_forces = contact_force_tensor.view(self.num_envs, bodies_per_env, 3)[..., :self.num_bodies, :] + + self._terminate_buf = torch.ones(self.num_envs, device=self.device, dtype=torch.long) + + self._build_termination_heights() + + contact_bodies = self.cfg["env"]["contactBodies"] + self._key_body_ids = self._build_key_body_ids_tensor(key_bodies) + self._contact_body_ids = self._build_contact_body_ids_tensor(contact_bodies) + + if self.viewer is not None: + self._init_camera() + + def get_obs_size(self): + return self._num_obs + + def get_action_size(self): + return self._num_actions + + def get_num_actors_per_env(self): + num_actors = self._root_states.shape[0] // self.num_envs + return num_actors + + def create_sim(self, **kwargs): + # self.up_axis_idx = self.set_sim_params_up_axis(self.sim_params, 'z') + self.up_axis_idx = 2 # index of up axis: Y=1, Z=2 + self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params) + + self._create_ground_plane() + self._create_envs(self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs))) + + # If randomizing, apply once immediately on startup before the fist sim step + if self.randomize: + self.apply_randomizations(self.randomization_params) + + def reset_idx(self, env_ids): + self._reset_actors(env_ids) + self._reset_env_tensors(env_ids) + self._refresh_sim_tensors() + self._compute_observations(env_ids) + + def _reset_actors(self, env_ids): + self._humanoid_root_states[env_ids] = self._initial_humanoid_root_states[env_ids] + self._dof_pos[env_ids] = self._initial_dof_pos[env_ids] + self._dof_vel[env_ids] = self._initial_dof_vel[env_ids] + + def _reset_env_tensors(self, env_ids): + env_ids_int32 = self._humanoid_actor_ids[env_ids] + self.gym.set_actor_root_state_tensor_indexed(self.sim, + gymtorch.unwrap_tensor(self._root_states), + gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32)) + self.gym.set_dof_state_tensor_indexed(self.sim, + gymtorch.unwrap_tensor(self._dof_state), + gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32)) + + self.progress_buf[env_ids] = 0 + self.reset_buf[env_ids] = 0 + self._terminate_buf[env_ids] = 0 + + def set_char_color(self, col): + for i in range(self.num_envs): + env_ptr = self.envs[i] + handle = self.humanoid_handles[i] + + for j in range(self.num_bodies): + self.gym.set_rigid_body_color(env_ptr, handle, j, gymapi.MESH_VISUAL, + gymapi.Vec3(col[0], col[1], col[2])) + + def _create_ground_plane(self): + plane_params = gymapi.PlaneParams() + plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0) + plane_params.static_friction = self.plane_static_friction + plane_params.dynamic_friction = self.plane_dynamic_friction + plane_params.restitution = self.plane_restitution + self.gym.add_ground(self.sim, plane_params) + + def _setup_character_props(self, key_bodies): + """ + dof_body_ids records the ids of the bodies that are connected to their parent bodies with joints + The order of these ids follows the define order of the body in the MJCF. The id start from 0, and + the body with id:0 is pelvis, which is not considered in the list. + + dof_offset's length is always len(dof_body_ids) + 1, and it always start from 0. + Each 2 values' minus in the list represents how many dofs that corresponding body have. + + dof_observation_size is equal to dof * 6, where 6 stands for position and rotation observations, dof is the + number of actuated dofs, it equals to the length of dof_body_ids + + num_actions is equal to the number of actuatable joints' number in the character. It does not include the + joint connecting the character to the world. + dof_observation_size + + num_observations is composed by 3 parts, the first observation is the height of the CoM of the character; the + second part is the observations for all bodies. The body number is multiplied by (3 position values, 6 + orientation values, 3 linear velocity, and 3 angular velocity); finally, -3 stands for + + :param key_bodies: + """ + # asset_body_num = self.cfg["env"]["asset"]["assetBodyNum"] + # asset_joint_num = self.cfg["env"]["asset"]["assetJointNum"] + asset_file = self.cfg["env"]["asset"]["assetFileName"] + # num_key_bodies = len(key_bodies) + + # The following are: body_name (body_id/body's joint num to its parent/offset pair) + # if asset_body_num == 15: + # if asset_joint_num == 28: + # # torso (1/3/0 3), head (2/3/3 6), right_upper_arm (3/3/6 9), right_lower_arm (4/1/9 10), + # # right_hand (5/0, omitted as no joint to parent) + # # left_upper_arm (6/3/10 13), left_lower_arm (7/1/13 14), left_hand (8/0), right_thigh (9/3/14 17), + # # right_shin (10/1/17 18), right_foot (11/3/18 21), left_thigh (12/3/21 24), left_shin (13/1/24 25), + # # left_foot (14/3/25 28) + # self._dof_body_ids = [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14] # len=12 + # self._dof_offsets = [0, 3, 6, 9, 10, 13, 14, 17, 18, 21, 24, 25, 28] # len=12+1 + # self._dof_obs_size = 72 + # self._num_actions = 28 + # self._num_obs = 1 + 15 * (3 + 6 + 3 + 3) - 3 + # elif asset_joint_num == 34: + # # torso (1/3/0 3), head (2/3/3 6), + # # right_upper_arm (3/3/6 9), right_lower_arm (4/1/9 10), right_hand (5/3/10 13) + # # left_upper_arm (6/3/13 16), left_lower_arm (7/1/16 17), left_hand (8/3/17 20), + # # right_thigh (9/3/20 23), right_shin (10/1/23 24), right_foot (11/3/24 27), + # # left_thigh (12/3/27 30), left_shin (13/1/30 31), left_foot (14/3/31 34) + # self._dof_body_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] # len=14 + # self._dof_offsets = [0, 3, 6, 9, 10, 13, 16, 17, 20, 23, 24, 27, 30, 31, 34] # len=14+1 + # self._dof_obs_size = 84 + # self._num_actions = 34 + # self._num_obs = 1 + 15 * (3 + 6 + 3 + 3) - 3 + # else: + # raise NotImplementedError + # elif asset_body_num == 16: + # self._dof_body_ids = [1, 2, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 16] + # self._dof_offsets = [0, 3, 6, 9, 10, 13, 16, 17, 20, 21, 24, 27, 28, 31] + # self._dof_obs_size = 78 + # self._num_actions = 31 + # self._num_obs = 1 + 17 * (3 + 6 + 3 + 3) - 3 + # elif asset_body_num == 17: + # if asset_joint_num == 34: + # # The following are: body_name (body_id/body's joint num to its parent/offset pair) + # # torso (1/3/0 3), head (2/3/3 6), right_upper_arm (3/3/6 9), right_lower_arm (4/1/9 10), + # # right_hand (5/3/10 13), spoon (6/0), left_upper_arm (7/3/13 16), left_lower_arm (8/1/16 17), + # # left_hand (9/3/17 20), pan (10/0), right_thigh (11/3/20 23), right_shin (12/1/23 24), + # # right_foot (13/3/24 27), left_thigh (14/3/27 30), left_shin (15/1/30 31), left_foot (16/3/31 34) + # self._dof_body_ids = [1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16] + # self._dof_offsets = [0, 3, 6, 9, 10, 13, 16, 17, 20, 23, 24, 27, 30, 31, 34] + # self._dof_obs_size = 84 + # self._num_actions = 34 + # self._num_obs = 1 + 17 * (3 + 6 + 3 + 3) - 3 + # elif asset_joint_num == 38: + # # torso (1/3/0 3), head (2/3/3 6), right_upper_arm (3/3/6 9), right_lower_arm (4/3/9 12), + # # right_hand (5/3/12 15), spoon (6/0), left_upper_arm (7/3/15 18), left_lower_arm (8/3/18 21), + # # left_hand (9/3/21 24), pan (10/0), right_thigh (11/3/24 27), right_shin (12/1/27 28), + # # right_foot (13/3/28 31), left_thigh (14/3/31 34), left_shin (15/1/34 35), left_foot (16/3/35 38) + # self._dof_body_ids = [1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16] + # self._dof_offsets = [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 28, 31, 34, 35, 38] + # self._dof_obs_size = 84 + # self._num_actions = 38 + # self._num_obs = 1 + 17 * (3 + 6 + 3 + 3) - 3 + # elif asset_body_num == 19: + # if asset_joint_num == 44: + # # torso (1/3/0 3), head (2/3/3 6), + # # right_shoulder(3/3/6 9), right_upper_arm (4/3/9 12), right_lower_arm (5/3/12 15), + # # right_hand (6/3/15 18), spoon (7/0), + # # left_shoulder(8/3/18 21), left_upper_arm (9/3/21 24), left_lower_arm (10/3/24 27), + # # left_hand (11/3/27 30), pan (12/0), right_thigh (13/3/30 33), right_shin (14/1/33 34), + # # right_foot (15/3/34 37), left_thigh (16/3/37 40), left_shin (17/1/40 41), left_foot (18/3/41 44) + # self._dof_body_ids = [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18] + # self._dof_offsets = [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 34, 37, 40, 41, 44] + # self._dof_obs_size = 96 + # self._num_actions = 44 + # self._num_obs = 1 + 19 * (3 + 6 + 3 + 3) - 3 + if asset_file == "mjcf/amp_humanoid.xml": + # torso (1/3/0 3), head (2/3/3 6), right_upper_arm (3/3/6 9), right_lower_arm (4/1/9 10), + # right_hand (5/0, omitted as no joint to parent) + # left_upper_arm (6/3/10 13), left_lower_arm (7/1/13 14), left_hand (8/0), right_thigh (9/3/14 17), + # right_shin (10/1/17 18), right_foot (11/3/18 21), left_thigh (12/3/21 24), left_shin (13/1/24 25), + # left_foot (14/3/25 28) + self._dof_body_ids = [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14] # len=12 + self._dof_offsets = [0, 3, 6, 9, 10, 13, 14, 17, 18, 21, 24, 25, 28] # len=12+1 + self._dof_obs_size = 72 # 12 * 6 (joint_obs_size) = 72 + self._num_actions = 28 + self._num_obs = 1 + 15 * (3 + 6 + 3 + 3) - 3 + elif asset_file == "mjcf/amp_humanoid_sword_shield.xml": + self._dof_body_ids = [1, 2, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 16] + self._dof_offsets = [0, 3, 6, 9, 10, 13, 16, 17, 20, 21, 24, 27, 28, 31] + self._dof_obs_size = 78 + self._num_actions = 31 + self._num_obs = 1 + 17 * (3 + 6 + 3 + 3) - 3 + elif asset_file in ["mjcf/amp_humanoid_spoon_pan_fixed.xml", "mjcf/hotu_humanoid.xml"]: + self._dof_body_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] # len=14 + self._dof_offsets = [0, 3, 6, 9, 10, 13, 16, 17, 20, 23, 24, 27, 30, 31, 34] # len=14+1 + self._dof_obs_size = 84 # 14 * 6 (joint_obs_size) = 72 + self._num_actions = 34 + self._num_obs = 1 + 15 * (3 + 6 + 3 + 3) - 3 + elif asset_file in "mjcf/hotu_humanoid_w_qbhand.xml": + self._dof_body_ids = [*[i for i in range(1, 6)], 7, 9, 11, 14, 16, 18, 21, 23, 25, 28, 30, 32, 35, 37, 39, + *[i for i in range(40, 43)], 44, 46, 48, 51, 53, 55, 58, 60, 62, 65, 67, 69, 72, 74, + 76, *[i for i in range(77, 83)]] # len=44 + self._dof_offsets = [0, 3, 6, 9, 10, *[i for i in range(13, 28)], 28, 31, 32, *[i for i in range(35, 50)], + 50, 53, 54, 57, 60, 61, 64] # len=44+1 + self._dof_obs_size = 264 # 44 * 6 (joint_obs_size) = 264 + self._num_actions = 64 + self._num_obs = 1 + 83 * (3 + 6 + 3 + 3) - 3 # 1243 + elif asset_file in ["mjcf/hotu_humanoid_w_qbhand_no_virtual.xml", + "mjcf/hotu_humanoid_w_qbhand_no_virtual_no_quat.xml"]: + self._dof_body_ids = [*[i for i in range(1, 45)]] # len=44 + self._dof_offsets = [0, 3, 6, 9, 10, *[i for i in range(13, 28)], 28, 31, 32, *[i for i in range(35, 50)], + 50, 53, 54, 57, 60, 61, 64] # len=44+1 + self._dof_obs_size = 264 # 44 * 6 (joint_obs_size) = 264 + self._num_actions = 64 + self._num_obs = 1 + 45 * (3 + 6 + 3 + 3) - 3 # 673 + else: + raise rf.logger.beauty_print(f"Unsupported character config file: {asset_file}") + + def _build_termination_heights(self): + head_term_height = 0.3 + shield_term_height = 0.32 + + termination_height = self.cfg["env"]["terminationHeight"] + self._termination_heights = np.array([termination_height] * self.num_bodies) + + head_id = self.gym.find_actor_rigid_body_handle(self.envs[0], self.humanoid_handles[0], "head") + self._termination_heights[head_id] = max(head_term_height, self._termination_heights[head_id]) + + asset_file = self.cfg["env"]["asset"]["assetFileName"] + if asset_file == "mjcf/amp_humanoid_sword_shield.xml": + left_arm_id = self.gym.find_actor_rigid_body_handle( + self.envs[0], self.humanoid_handles[0], "left_lower_arm" + ) + self._termination_heights[left_arm_id] = max(shield_term_height, self._termination_heights[left_arm_id]) + + self._termination_heights = to_torch(self._termination_heights, device=self.device) + + def _create_envs(self, spacing, num_per_row): + lower = gymapi.Vec3(-spacing, -spacing, 0.0) + upper = gymapi.Vec3(spacing, spacing, spacing) + + # get rofunc path from rofunc package metadata + rofunc_path = get_rofunc_path() + asset_root = os.path.join(rofunc_path, "simulator/assets") + + # Load humanoid asset + asset_file = self.cfg["env"]["asset"]["assetFileName"] + asset_options = gymapi.AssetOptions() + asset_options.angular_damping = 0.01 + asset_options.max_angular_velocity = 100.0 + asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE + asset_options.disable_gravity = True + # asset_options.fix_base_link = True + humanoid_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options) + actuator_props = self.gym.get_asset_actuator_properties(humanoid_asset) + motor_efforts = [prop.motor_effort for prop in actuator_props] + + # create force sensors at the feet + right_foot_idx = self.gym.find_asset_rigid_body_index(humanoid_asset, "right_foot") + left_foot_idx = self.gym.find_asset_rigid_body_index(humanoid_asset, "left_foot") + sensor_pose = gymapi.Transform() + self.gym.create_asset_force_sensor(humanoid_asset, right_foot_idx, sensor_pose) + self.gym.create_asset_force_sensor(humanoid_asset, left_foot_idx, sensor_pose) + self.max_motor_effort = max(motor_efforts) + self.motor_efforts = to_torch(motor_efforts, device=self.device) + + # Load object assets + object_asset_files = self.cfg["env"]["object_asset"]["assetFileName"] + self.object_names = self.cfg["env"]["object_asset"]["assetName"] + if self.object_names is not None: + object_assets = {} + for i in range(len(self.object_names)): + asset_options = gymapi.AssetOptions() + asset_options.angular_damping = 0.01 + asset_options.max_angular_velocity = 100.0 + asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE + # asset_options.disable_gravity = True + # asset_options.fix_base_link = True + + if 'box' in self.object_names[i].lower(): + object_size = self.cfg["env"]["object_asset"]["assetSize"][i] + object_asset = self.gym.create_box(self.sim, *object_size, asset_options) + elif self.object_names[i].lower() == 'sphere': + object_radius = self.cfg["env"]["object_asset"]["assetSize"][i] + object_asset = self.gym.create_sphere(self.sim, object_radius, asset_options) + else: + object_asset = self.gym.load_asset(self.sim, asset_root, object_asset_files[i], asset_options) + object_assets[self.object_names[i]] = object_asset + else: + object_assets = None + + self.torso_index = 0 + self.num_bodies = self.gym.get_asset_rigid_body_count(humanoid_asset) + self.num_dof = self.gym.get_asset_dof_count(humanoid_asset) + self.num_joints = self.gym.get_asset_joint_count(humanoid_asset) + + self.humanoid_handles = [] + self.object_handles = {} + self.envs = [] + self.dof_limits_lower = [] + self.dof_limits_upper = [] + + for i in range(self.num_envs): + # create env instance + env_ptr = self.gym.create_env(self.sim, lower, upper, num_per_row) + self._build_env(i, env_ptr, humanoid_asset, object_assets) + self.envs.append(env_ptr) + + # Set humanoid dof + dof_prop = self.gym.get_actor_dof_properties(self.envs[0], self.humanoid_handles[0]) + for j in range(self.num_dof): + if dof_prop["lower"][j] > dof_prop["upper"][j]: + self.dof_limits_lower.append(dof_prop["upper"][j]) + self.dof_limits_upper.append(dof_prop["lower"][j]) + else: + self.dof_limits_lower.append(dof_prop["lower"][j]) + self.dof_limits_upper.append(dof_prop["upper"][j]) + + self.dof_limits_lower = to_torch(self.dof_limits_lower, device=self.device) + self.dof_limits_upper = to_torch(self.dof_limits_upper, device=self.device) + + if self._pd_control: + self._build_pd_action_offset_scale() + + def _build_env(self, env_id, env_ptr, humanoid_asset, object_assets=None): + col_group = env_id + col_filter = self._get_humanoid_collision_filter() + segmentation_id = 0 + + start_pose = gymapi.Transform() + char_h = 0.89 + + start_pose.p = gymapi.Vec3(*get_axis_params(char_h, self.up_axis_idx)) + start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0) + + humanoid_handle = self.gym.create_actor( + env_ptr, + humanoid_asset, + start_pose, + "humanoid", + col_group, + col_filter, + segmentation_id, + ) + + self.gym.enable_actor_dof_force_sensors(env_ptr, humanoid_handle) + + for j in range(self.num_bodies): + self.gym.set_rigid_body_color( + env_ptr, + humanoid_handle, + j, + gymapi.MESH_VISUAL, + gymapi.Vec3(0.54, 0.85, 0.2), + ) + + if self._pd_control: + dof_prop = self.gym.get_asset_dof_properties(humanoid_asset) + dof_prop["driveMode"] = gymapi.DOF_MODE_POS + self.gym.set_actor_dof_properties(env_ptr, humanoid_handle, dof_prop) + + self.humanoid_handles.append(humanoid_handle) + + if object_assets is not None: + for object_name, object_asset in object_assets.items(): + self._add_object(env_id, env_ptr, object_name, object_asset) + + def _add_object(self, env_id, env_ptr, object_name, object_asset): + start_pose = gymapi.Transform() + char_h = 0.5 + + start_pose.p = gymapi.Vec3(*get_axis_params(char_h, self.up_axis_idx)) + start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0) + + col_group = 100000 if object_name is 'base' else env_id + + object_handle = self.gym.create_actor( + env_ptr, + object_asset, + start_pose, + object_name, + col_group, + 1, + 0, + ) + if object_name not in self.object_handles: + self.object_handles[object_name] = [] + self.object_handles[object_name].append(object_handle) + self.gym.set_rigid_body_color( + env_ptr, + object_handle, + 0, + gymapi.MESH_VISUAL, + gymapi.Vec3(0.5, 0.2, 0.0), + ) + + def _build_pd_action_offset_scale(self): + num_joints = len(self._dof_offsets) - 1 + + lim_low = self.dof_limits_lower.cpu().numpy() + lim_high = self.dof_limits_upper.cpu().numpy() + + for j in range(num_joints): + dof_offset = self._dof_offsets[j] + dof_size = self._dof_offsets[j + 1] - self._dof_offsets[j] + + if dof_size == 3: + curr_low = lim_low[dof_offset: (dof_offset + dof_size)] + curr_high = lim_high[dof_offset: (dof_offset + dof_size)] + curr_low = np.max(np.abs(curr_low)) + curr_high = np.max(np.abs(curr_high)) + curr_scale = max([curr_low, curr_high]) + curr_scale = 1.2 * curr_scale + curr_scale = min([curr_scale, np.pi]) + + lim_low[dof_offset: (dof_offset + dof_size)] = -curr_scale + lim_high[dof_offset: (dof_offset + dof_size)] = curr_scale + + # lim_low[dof_offset:(dof_offset + dof_size)] = -np.pi + # lim_high[dof_offset:(dof_offset + dof_size)] = np.pi + + elif dof_size == 1: + curr_low = lim_low[dof_offset] + curr_high = lim_high[dof_offset] + curr_mid = 0.5 * (curr_high + curr_low) + + # extend the action range to be a bit beyond the joint limits so that the motors + # don't lose their strength as they approach the joint limits + curr_scale = 0.7 * (curr_high - curr_low) + curr_low = curr_mid - curr_scale + curr_high = curr_mid + curr_scale + + lim_low[dof_offset] = curr_low + lim_high[dof_offset] = curr_high + + self._pd_action_offset = 0.5 * (lim_high + lim_low) + self._pd_action_scale = 0.5 * (lim_high - lim_low) + self._pd_action_offset = to_torch(self._pd_action_offset, device=self.device) + self._pd_action_scale = to_torch(self._pd_action_scale, device=self.device) + + def _get_humanoid_collision_filter(self): + return 0 + + def _compute_reward(self, actions): + self.rew_buf[:] = compute_humanoid_reward(self.obs_buf) + + def _compute_reset(self): + self.reset_buf[:], self._terminate_buf[:] = compute_humanoid_reset( + self.reset_buf, + self.progress_buf, + self._contact_forces, + self._contact_body_ids, + self._rigid_body_pos, + self.max_episode_length, + self._enable_early_termination, + self._termination_heights, + ) + + def _refresh_sim_tensors(self): + self.gym.refresh_dof_state_tensor(self.sim) + self.gym.refresh_actor_root_state_tensor(self.sim) + self.gym.refresh_rigid_body_state_tensor(self.sim) + + self.gym.refresh_force_sensor_tensor(self.sim) + self.gym.refresh_dof_force_tensor(self.sim) + self.gym.refresh_net_contact_force_tensor(self.sim) + + def _compute_observations(self, env_ids=None): + obs = self._compute_humanoid_obs(env_ids) + + if env_ids is None: + self.obs_buf[:] = obs + else: + self.obs_buf[env_ids] = obs + + def _compute_humanoid_obs(self, env_ids=None): + if env_ids is None: + body_pos = self._rigid_body_pos + body_rot = self._rigid_body_rot + body_vel = self._rigid_body_vel + body_ang_vel = self._rigid_body_ang_vel + else: + body_pos = self._rigid_body_pos[env_ids] + body_rot = self._rigid_body_rot[env_ids] + body_vel = self._rigid_body_vel[env_ids] + body_ang_vel = self._rigid_body_ang_vel[env_ids] + + obs = compute_humanoid_observations_max( + body_pos, + body_rot, + body_vel, + body_ang_vel, + self._local_root_obs, + self._root_height_obs, + ) + return obs + + def pre_physics_step(self, actions): + self.actions = actions.to(self.device).clone() + + if self._pd_control: + pd_tar = self._action_to_pd_targets(self.actions) + pd_tar_tensor = gymtorch.unwrap_tensor(pd_tar) + self.gym.set_dof_position_target_tensor(self.sim, pd_tar_tensor) + else: + forces = self.actions * self.motor_efforts.unsqueeze(0) * self.power_scale + force_tensor = gymtorch.unwrap_tensor(forces) + self.gym.set_dof_actuation_force_tensor(self.sim, force_tensor) + + def post_physics_step(self): + self.progress_buf += 1 + + self._refresh_sim_tensors() + self._compute_observations() + self._compute_reward(self.actions) + self._compute_reset() + + self.extras["terminate"] = self._terminate_buf + + # debug viz + if self.viewer and self.debug_viz: + self._update_debug_viz() + + def render(self, sync_frame_time=False): + if self.viewer and self.camera_follow: + self._update_camera() + + super().render(sync_frame_time) + + def _build_key_body_ids_tensor(self, key_body_names): + env_ptr = self.envs[0] + actor_handle = self.humanoid_handles[0] + body_ids = [] + + for body_name in key_body_names: + body_id = self.gym.find_actor_rigid_body_handle( + env_ptr, actor_handle, body_name + ) + assert body_id != -1 + body_ids.append(body_id) + + body_ids = to_torch(body_ids, device=self.device, dtype=torch.long) + return body_ids + + def _build_contact_body_ids_tensor(self, contact_body_names): + env_ptr = self.envs[0] + actor_handle = self.humanoid_handles[0] + body_ids = [] + + for body_name in contact_body_names: + body_id = self.gym.find_actor_rigid_body_handle( + env_ptr, actor_handle, body_name + ) + assert body_id != -1 + body_ids.append(body_id) + + body_ids = to_torch(body_ids, device=self.device, dtype=torch.long) + return body_ids + + def _action_to_pd_targets(self, action): + pd_tar = self._pd_action_offset + self._pd_action_scale * action + return pd_tar + + def _init_camera(self): + self.gym.refresh_actor_root_state_tensor(self.sim) + self._cam_prev_char_pos = self._humanoid_root_states[0, 0:3].cpu().numpy() + + cam_pos = gymapi.Vec3( + self._cam_prev_char_pos[0], self._cam_prev_char_pos[1] - 3.0, 1.0 + ) + cam_target = gymapi.Vec3( + self._cam_prev_char_pos[0], self._cam_prev_char_pos[1], 1.0 + ) + if self.cfg["sim"]["up_axis"] == "y": + cam_pos = gymapi.Vec3( + self._cam_prev_char_pos[0], + 1.0, + self._cam_prev_char_pos[1] - 3.0, + ) + cam_target = gymapi.Vec3( + self._cam_prev_char_pos[0], + 1.0, + self._cam_prev_char_pos[1], + ) + self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target) + + def _update_camera(self): + self.gym.refresh_actor_root_state_tensor(self.sim) + char_root_pos = self._humanoid_root_states[0, 0:3].cpu().numpy() + + cam_trans = self.gym.get_viewer_camera_transform(self.viewer, None) + cam_pos = np.array([cam_trans.p.x, cam_trans.p.y, cam_trans.p.z]) + cam_delta = cam_pos - self._cam_prev_char_pos + + new_cam_target = gymapi.Vec3(char_root_pos[0], char_root_pos[1], 1.0) + new_cam_pos = gymapi.Vec3( + char_root_pos[0] + cam_delta[0], char_root_pos[1] + cam_delta[1], cam_pos[2] + ) + + if self.cfg["sim"]["up_axis"] == "y": + new_cam_target = gymapi.Vec3(char_root_pos[0], 1.0, char_root_pos[1]) + new_cam_pos = gymapi.Vec3( + char_root_pos[0] + cam_delta[0], + cam_pos[2], + char_root_pos[1] + cam_delta[1], + ) + + self.gym.viewer_camera_look_at(self.viewer, None, new_cam_pos, new_cam_target) + + self._cam_prev_char_pos[:] = char_root_pos + return + + def _update_debug_viz(self): + self.gym.clear_lines(self.viewer) + + +class HumanoidHOTU(Humanoid): + class StateInit(Enum): + Default = 0 + Start = 1 + Random = 2 + Hybrid = 3 + + def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render): + self.cfg = cfg + + state_init = cfg["env"]["stateInit"] + self._state_init = HumanoidHOTU.StateInit[state_init] + self._hybrid_init_prob = cfg["env"]["hybridInitProb"] + self._num_amp_obs_steps = cfg["env"]["numAMPObsSteps"] + assert self._num_amp_obs_steps >= 2 + + self._reset_default_env_ids = [] + self._reset_ref_env_ids = [] + + super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, + graphics_device_id=graphics_device_id, headless=headless, + virtual_screen_capture=virtual_screen_capture, force_render=force_render) + + # Load motion file + motion_file = cfg["env"].get("motion_file", None) + if rf.oslab.is_absl_path(motion_file): + motion_file_path = motion_file + elif motion_file.split("/")[0] == "examples": + motion_file_path = os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "../../../../../../" + motion_file, + ) + else: + raise ValueError("Unsupported motion file path") + self._load_motion(motion_file_path) + + # Load object motion file + object_motion_file = cfg["env"].get("object_motion_file", None) + if object_motion_file is not None: + if rf.oslab.is_absl_path(object_motion_file): + object_motion_file_path = object_motion_file + elif object_motion_file.split("/")[0] == "examples": + object_motion_file_path = os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "../../../../../../" + object_motion_file, + ) + else: + raise ValueError("Unsupported object motion file path") + self._load_object_motion(object_motion_file_path) + + # Set up the observation space for AMP + self._amp_obs_space = spaces.Box(np.ones(self.get_num_amp_obs()) * -np.Inf, + np.ones(self.get_num_amp_obs()) * np.Inf) + self._amp_obs_buf = torch.zeros((self.num_envs, self._num_amp_obs_steps, self._num_amp_obs_per_step), + device=self.device, dtype=torch.float) + self._curr_amp_obs_buf = self._amp_obs_buf[:, 0] + self._hist_amp_obs_buf = self._amp_obs_buf[:, 1:] + + self._amp_obs_demo_buf = None + + def post_physics_step(self): + super().post_physics_step() + + self._update_hist_amp_obs() + self._compute_amp_observations() + + amp_obs_flat = self._amp_obs_buf.view(-1, self.get_num_amp_obs()) + self.extras["amp_obs"] = amp_obs_flat + + def get_num_amp_obs(self): + return self._num_amp_obs_steps * self._num_amp_obs_per_step + + @property + def amp_observation_space(self): + return self._amp_obs_space + + def fetch_amp_obs_demo(self, num_samples): + return self.task.fetch_amp_obs_demo(num_samples) + + def fetch_amp_obs_demo(self, num_samples): + dt = self.dt + motion_ids = self._motion_lib.sample_motions(num_samples) + + if self._amp_obs_demo_buf is None: + self._build_amp_obs_demo_buf(num_samples) + else: + assert self._amp_obs_demo_buf.shape[0] == num_samples + + # since negative times are added to these values in build_amp_obs_demo, + # we shift them into the range [0 + truncate_time, end of clip] + truncate_time = self.dt * (self._num_amp_obs_steps - 1) + motion_times0 = self._motion_lib.sample_time(motion_ids, truncate_time=truncate_time) + motion_times0 += truncate_time + + amp_obs_demo = self.build_amp_obs_demo(motion_ids, motion_times0) + self._amp_obs_demo_buf[:] = amp_obs_demo.view(self._amp_obs_demo_buf.shape) + amp_obs_demo_flat = self._amp_obs_demo_buf.view(-1, self.get_num_amp_obs()) + + return amp_obs_demo_flat + + def build_amp_obs_demo(self, motion_ids, motion_times0): + dt = self.dt + + motion_ids = torch.tile(motion_ids.unsqueeze(-1), [1, self._num_amp_obs_steps]) + motion_times = motion_times0.unsqueeze(-1) + time_steps = -dt * torch.arange(0, self._num_amp_obs_steps, device=self.device) + motion_times = motion_times + time_steps + + motion_ids = motion_ids.view(-1) + motion_times = motion_times.view(-1) + root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel, key_pos, _, _ \ + = self._motion_lib.get_motion_state(motion_ids, motion_times) + amp_obs_demo = build_amp_observations(root_pos, root_rot, root_vel, root_ang_vel, + dof_pos, dof_vel, key_pos, + self._local_root_obs, self._root_height_obs, + self._dof_obs_size, self._dof_offsets) + return amp_obs_demo + + def _build_amp_obs_demo_buf(self, num_samples): + self._amp_obs_demo_buf = torch.zeros((num_samples, self._num_amp_obs_steps, self._num_amp_obs_per_step), + device=self.device, dtype=torch.float32) + + def _setup_character_props(self, key_bodies): + super()._setup_character_props(key_bodies) + + asset_file = self.cfg["env"]["asset"]["assetFileName"] + num_key_bodies = len(key_bodies) + + if asset_file == "mjcf/amp_humanoid.xml": + self._num_amp_obs_per_step = 13 + self._dof_obs_size + 28 + 3 * num_key_bodies # [root_h, root_rot, root_vel, root_ang_vel, dof_pos, dof_vel, key_body_pos] + elif asset_file == "mjcf/amp_humanoid_sword_shield.xml": + self._num_amp_obs_per_step = 13 + self._dof_obs_size + 31 + 3 * num_key_bodies # [root_h, root_rot, root_vel, root_ang_vel, dof_pos, d + elif asset_file in ["mjcf/amp_humanoid_spoon_pan_fixed.xml", "mjcf/hotu_humanoid.xml"]: + self._num_amp_obs_per_step = 13 + self._dof_obs_size + 34 + 3 * num_key_bodies + elif asset_file == "mjcf/hotu_humanoid_w_qbhand.xml": + self._num_amp_obs_per_step = 13 + self._dof_obs_size + 64 + 3 * num_key_bodies + elif asset_file in ["mjcf/hotu_humanoid_w_qbhand_no_virtual.xml", + "mjcf/hotu_humanoid_w_qbhand_no_virtual_no_quat.xml"]: + self._num_amp_obs_per_step = 13 + self._dof_obs_size + 64 + 3 * num_key_bodies + else: + print(f"Unsupported humanoid body num: {asset_file}") + assert False + + def _load_motion(self, motion_file): + assert self._dof_offsets[-1] == self.num_dof + self._motion_lib = MotionLib( + motion_file=motion_file, + dof_body_ids=self._dof_body_ids, + dof_offsets=self._dof_offsets, + key_body_ids=self._key_body_ids.cpu().numpy(), + device=self.device, + ) + + def _load_object_motion(self, object_motion_file): + self._object_motion_lib = ObjectMotionLib( + object_motion_file=object_motion_file, + object_names=self.cfg["env"]["object_asset"]["assetName"], + device=self.device, + height_offset=0 + ) + + def reset_idx(self, env_ids): + self._reset_default_env_ids = [] + self._reset_ref_env_ids = [] + + super().reset_idx(env_ids) + self._init_amp_obs(env_ids) + + def _reset_actors(self, env_ids): + if self._state_init == HumanoidHOTU.StateInit.Default: + self._reset_default(env_ids) + elif ( + self._state_init == HumanoidHOTU.StateInit.Start + or self._state_init == HumanoidHOTU.StateInit.Random + ): + self._reset_ref_state_init(env_ids) + elif self._state_init == HumanoidHOTU.StateInit.Hybrid: + self._reset_hybrid_state_init(env_ids) + else: + assert False, "Unsupported state initialization strategy: {:s}".format( + str(self._state_init) + ) + + def _reset_default(self, env_ids): + self._humanoid_root_states[env_ids] = self._initial_humanoid_root_states[ + env_ids + ] + self._dof_pos[env_ids] = self._initial_dof_pos[env_ids] + self._dof_vel[env_ids] = self._initial_dof_vel[env_ids] + self._reset_default_env_ids = env_ids + + def _reset_ref_state_init(self, env_ids): + num_envs = env_ids.shape[0] + motion_ids = self._motion_lib.sample_motions(num_envs) + + if ( + self._state_init == HumanoidHOTU.StateInit.Random + or self._state_init == HumanoidHOTU.StateInit.Hybrid + ): + motion_times = self._motion_lib.sample_time(motion_ids) + elif self._state_init == HumanoidHOTU.StateInit.Start: + motion_times = torch.zeros(num_envs, device=self.device) + else: + assert ( + False + ), f"Unsupported state initialization strategy: {self._state_init}" + + ( + root_pos, + root_rot, + dof_pos, + root_vel, + root_ang_vel, + dof_vel, + key_pos, + ) = self._motion_lib.get_motion_state(motion_ids, motion_times) + + self._set_env_state( + env_ids=env_ids, + root_pos=root_pos, + root_rot=root_rot, + dof_pos=dof_pos, + root_vel=root_vel, + root_ang_vel=root_ang_vel, + dof_vel=dof_vel, + ) + + self._reset_ref_env_ids = env_ids + self._reset_ref_motion_ids = motion_ids + self._reset_ref_motion_times = motion_times + + def _reset_hybrid_state_init(self, env_ids): + num_envs = env_ids.shape[0] + ref_probs = to_torch( + np.array([self._hybrid_init_prob] * num_envs), device=self.device + ) + ref_init_mask = torch.bernoulli(ref_probs) == 1.0 + + ref_reset_ids = env_ids[ref_init_mask] + if len(ref_reset_ids) > 0: + self._reset_ref_state_init(ref_reset_ids) + + default_reset_ids = env_ids[torch.logical_not(torch.tensor(ref_init_mask))] + if len(default_reset_ids) > 0: + self._reset_default(default_reset_ids) + + def _init_amp_obs(self, env_ids): + self._compute_amp_observations(env_ids) + + if len(self._reset_default_env_ids) > 0: + self._init_amp_obs_default(self._reset_default_env_ids) + + if len(self._reset_ref_env_ids) > 0: + self._init_amp_obs_ref( + self._reset_ref_env_ids, + self._reset_ref_motion_ids, + self._reset_ref_motion_times, + ) + + def _init_amp_obs_default(self, env_ids): + curr_amp_obs = self._curr_amp_obs_buf[env_ids].unsqueeze(-2) + self._hist_amp_obs_buf[env_ids] = curr_amp_obs + + def _init_amp_obs_ref(self, env_ids, motion_ids, motion_times): + dt = self.dt + motion_ids = torch.tile( + motion_ids.unsqueeze(-1), [1, self._num_amp_obs_steps - 1] + ) + motion_times = motion_times.unsqueeze(-1) + time_steps = -dt * ( + torch.arange(0, self._num_amp_obs_steps - 1, device=self.device) + 1 + ) + motion_times = motion_times + time_steps + + motion_ids = motion_ids.view(-1) + motion_times = motion_times.view(-1) + ( + root_pos, + root_rot, + dof_pos, + root_vel, + root_ang_vel, + dof_vel, + key_pos, + ) = self._motion_lib.get_motion_state(motion_ids, motion_times) + amp_obs_demo = build_amp_observations( + root_pos, + root_rot, + root_vel, + root_ang_vel, + dof_pos, + dof_vel, + key_pos, + self._local_root_obs, + self._root_height_obs, + self._dof_obs_size, + self._dof_offsets, + ) + self._hist_amp_obs_buf[env_ids] = amp_obs_demo.view( + self._hist_amp_obs_buf[env_ids].shape + ) + + def _set_env_state( + self, env_ids, root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel + ): + self._humanoid_root_states[env_ids, 0:3] = root_pos + self._humanoid_root_states[env_ids, 3:7] = root_rot + self._humanoid_root_states[env_ids, 7:10] = root_vel + self._humanoid_root_states[env_ids, 10:13] = root_ang_vel + + # self._dof_pos[env_ids] = dof_pos + self.init_dof_pose.to('cuda:0') + # self._dof_pos[env_ids] = torch.zeros_like(dof_pos).to('cuda:0') + self._dof_pos[env_ids] = dof_pos + # self._dof_pos[env_ids, 6] = -1 + # self._dof_pos[env_ids, 28] = 1 + self._dof_vel[env_ids] = dof_vel + + def _update_hist_amp_obs(self, env_ids=None): + if env_ids is None: + for i in reversed(range(self._amp_obs_buf.shape[1] - 1)): + self._amp_obs_buf[:, i + 1] = self._amp_obs_buf[:, i] + else: + for i in reversed(range(self._amp_obs_buf.shape[1] - 1)): + self._amp_obs_buf[env_ids, i + 1] = self._amp_obs_buf[env_ids, i] + + def _compute_amp_observations(self, env_ids=None): + key_body_pos = self._rigid_body_pos[:, self._key_body_ids, :] + if env_ids is None: + self._curr_amp_obs_buf[:] = build_amp_observations( + self._rigid_body_pos[:, 0, :], + self._rigid_body_rot[:, 0, :], + self._rigid_body_vel[:, 0, :], + self._rigid_body_ang_vel[:, 0, :], + self._dof_pos, + self._dof_vel, + key_body_pos, + self._local_root_obs, + self._root_height_obs, + self._dof_obs_size, + self._dof_offsets, + ) + else: + self._curr_amp_obs_buf[env_ids] = build_amp_observations( + self._rigid_body_pos[env_ids][:, 0, :], + self._rigid_body_rot[env_ids][:, 0, :], + self._rigid_body_vel[env_ids][:, 0, :], + self._rigid_body_ang_vel[env_ids][:, 0, :], + self._dof_pos[env_ids], + self._dof_vel[env_ids], + key_body_pos[env_ids], + self._local_root_obs, + self._root_height_obs, + self._dof_obs_size, + self._dof_offsets, + ) + + +@torch.jit.script +def build_amp_observations( + root_pos, + root_rot, + root_vel, + root_ang_vel, + dof_pos, + dof_vel, + key_body_pos, + local_root_obs, + root_height_obs, + dof_obs_size, + dof_offsets, +): + # type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, bool, bool, int, List[int]) -> Tensor + root_h = root_pos[:, 2:3] + heading_rot = torch_utils.calc_heading_quat_inv(root_rot) + + if local_root_obs: + root_rot_obs = quat_mul(heading_rot, root_rot) + else: + root_rot_obs = root_rot + root_rot_obs = torch_utils.quat_to_tan_norm(root_rot_obs) + + if not root_height_obs: + root_h_obs = torch.zeros_like(root_h) + else: + root_h_obs = root_h + + local_root_vel = quat_rotate(heading_rot, root_vel) + local_root_ang_vel = quat_rotate(heading_rot, root_ang_vel) + + root_pos_expand = root_pos.unsqueeze(-2) + local_key_body_pos = key_body_pos - root_pos_expand + + heading_rot_expand = heading_rot.unsqueeze(-2) + heading_rot_expand = heading_rot_expand.repeat((1, local_key_body_pos.shape[1], 1)) + flat_end_pos = local_key_body_pos.view( + local_key_body_pos.shape[0] * local_key_body_pos.shape[1], + local_key_body_pos.shape[2], + ) + flat_heading_rot = heading_rot_expand.view( + heading_rot_expand.shape[0] * heading_rot_expand.shape[1], + heading_rot_expand.shape[2], + ) + local_end_pos = quat_rotate(flat_heading_rot, flat_end_pos) + flat_local_key_pos = local_end_pos.view( + local_key_body_pos.shape[0], + local_key_body_pos.shape[1] * local_key_body_pos.shape[2], + ) + + dof_obs = dof_to_obs(dof_pos, dof_obs_size, dof_offsets) + obs = torch.cat( + ( + root_h_obs, + root_rot_obs, + local_root_vel, + local_root_ang_vel, + dof_obs, + dof_vel, + flat_local_key_pos, + ), + dim=-1, + ) + return obs + + +##################################################################### +###=========================jit functions=========================### +##################################################################### + + +@torch.jit.script +def dof_to_obs(pose, dof_obs_size, dof_offsets): + # type: (Tensor, int, List[int]) -> Tensor + joint_obs_size = 6 + num_joints = len(dof_offsets) - 1 + + dof_obs_shape = pose.shape[:-1] + (dof_obs_size,) + dof_obs = torch.zeros(dof_obs_shape, device=pose.device) + dof_obs_offset = 0 + + for j in range(num_joints): + dof_offset = dof_offsets[j] + dof_size = dof_offsets[j + 1] - dof_offsets[j] + joint_pose = pose[:, dof_offset: (dof_offset + dof_size)] + + # assume this is a spherical joint + if dof_size == 3: + joint_pose_q = torch_utils.exp_map_to_quat(joint_pose) + elif dof_size == 1: + axis = torch.tensor( + [0.0, 1.0, 0.0], dtype=joint_pose.dtype, device=pose.device + ) + joint_pose_q = quat_from_angle_axis(joint_pose[..., 0], axis) + else: + joint_pose_q = None + assert False, "Unsupported joint type" + + joint_dof_obs = torch_utils.quat_to_tan_norm(joint_pose_q) + dof_obs[:, (j * joint_obs_size): ((j + 1) * joint_obs_size)] = joint_dof_obs + + assert (num_joints * joint_obs_size) == dof_obs_size + + return dof_obs + + +@torch.jit.script +def compute_humanoid_observations( + root_pos, + root_rot, + root_vel, + root_ang_vel, + dof_pos, + dof_vel, + key_body_pos, + local_root_obs, + root_height_obs, + dof_obs_size, + dof_offsets, +): + # type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, bool, bool, int, List[int]) -> Tensor + root_h = root_pos[:, 2:3] + heading_rot = torch_utils.calc_heading_quat_inv(root_rot) + + if local_root_obs: + root_rot_obs = quat_mul(heading_rot, root_rot) + else: + root_rot_obs = root_rot + root_rot_obs = torch_utils.quat_to_tan_norm(root_rot_obs) + + if not root_height_obs: + root_h_obs = torch.zeros_like(root_h) + else: + root_h_obs = root_h + + local_root_vel = quat_rotate(heading_rot, root_vel) + local_root_ang_vel = quat_rotate(heading_rot, root_ang_vel) + + root_pos_expand = root_pos.unsqueeze(-2) + local_key_body_pos = key_body_pos - root_pos_expand + + heading_rot_expand = heading_rot.unsqueeze(-2) + heading_rot_expand = heading_rot_expand.repeat((1, local_key_body_pos.shape[1], 1)) + flat_end_pos = local_key_body_pos.view( + local_key_body_pos.shape[0] * local_key_body_pos.shape[1], + local_key_body_pos.shape[2], + ) + flat_heading_rot = heading_rot_expand.view( + heading_rot_expand.shape[0] * heading_rot_expand.shape[1], + heading_rot_expand.shape[2], + ) + local_end_pos = quat_rotate(flat_heading_rot, flat_end_pos) + flat_local_key_pos = local_end_pos.view( + local_key_body_pos.shape[0], + local_key_body_pos.shape[1] * local_key_body_pos.shape[2], + ) + + dof_obs = dof_to_obs(dof_pos, dof_obs_size, dof_offsets) + + obs = torch.cat( + ( + root_h_obs, + root_rot_obs, + local_root_vel, + local_root_ang_vel, + dof_obs, + dof_vel, + flat_local_key_pos, + ), + dim=-1, + ) + return obs + + +@torch.jit.script +def compute_humanoid_observations_max( + body_pos, body_rot, body_vel, body_ang_vel, local_root_obs, root_height_obs +): + # type: (Tensor, Tensor, Tensor, Tensor, bool, bool) -> Tensor + root_pos = body_pos[:, 0, :] + root_rot = body_rot[:, 0, :] + + root_h = root_pos[:, 2:3] + heading_rot = torch_utils.calc_heading_quat_inv(root_rot) + + if not root_height_obs: + root_h_obs = torch.zeros_like(root_h) + else: + root_h_obs = root_h + + heading_rot_expand = heading_rot.unsqueeze(-2) + heading_rot_expand = heading_rot_expand.repeat((1, body_pos.shape[1], 1)) + flat_heading_rot = heading_rot_expand.reshape( + heading_rot_expand.shape[0] * heading_rot_expand.shape[1], + heading_rot_expand.shape[2], + ) + + root_pos_expand = root_pos.unsqueeze(-2) + local_body_pos = body_pos - root_pos_expand + flat_local_body_pos = local_body_pos.reshape( + local_body_pos.shape[0] * local_body_pos.shape[1], local_body_pos.shape[2] + ) + flat_local_body_pos = quat_rotate(flat_heading_rot, flat_local_body_pos) + local_body_pos = flat_local_body_pos.reshape( + local_body_pos.shape[0], local_body_pos.shape[1] * local_body_pos.shape[2] + ) + local_body_pos = local_body_pos[..., 3:] # remove root pos + + flat_body_rot = body_rot.reshape( + body_rot.shape[0] * body_rot.shape[1], body_rot.shape[2] + ) + flat_local_body_rot = quat_mul(flat_heading_rot, flat_body_rot) + flat_local_body_rot_obs = torch_utils.quat_to_tan_norm(flat_local_body_rot) + local_body_rot_obs = flat_local_body_rot_obs.reshape( + body_rot.shape[0], body_rot.shape[1] * flat_local_body_rot_obs.shape[1] + ) + + if local_root_obs: + root_rot_obs = torch_utils.quat_to_tan_norm(root_rot) + local_body_rot_obs[..., 0:6] = root_rot_obs + + flat_body_vel = body_vel.reshape( + body_vel.shape[0] * body_vel.shape[1], body_vel.shape[2] + ) + flat_local_body_vel = quat_rotate(flat_heading_rot, flat_body_vel) + local_body_vel = flat_local_body_vel.reshape( + body_vel.shape[0], body_vel.shape[1] * body_vel.shape[2] + ) + + flat_body_ang_vel = body_ang_vel.reshape( + body_ang_vel.shape[0] * body_ang_vel.shape[1], body_ang_vel.shape[2] + ) + flat_local_body_ang_vel = quat_rotate(flat_heading_rot, flat_body_ang_vel) + local_body_ang_vel = flat_local_body_ang_vel.reshape( + body_ang_vel.shape[0], body_ang_vel.shape[1] * body_ang_vel.shape[2] + ) + + obs = torch.cat( + ( + root_h_obs, + local_body_pos, + local_body_rot_obs, + local_body_vel, + local_body_ang_vel, + ), + dim=-1, + ) + return obs + + +@torch.jit.script +def compute_humanoid_reward(obs_buf): + # type: (Tensor) -> Tensor + reward = torch.ones_like(obs_buf[:, 0]) + return reward + + +@torch.jit.script +def compute_humanoid_reset( + reset_buf, + progress_buf, + contact_buf, + contact_body_ids, + rigid_body_pos, + max_episode_length, + enable_early_termination, + termination_heights, +): + # type: (Tensor, Tensor, Tensor, Tensor, Tensor, float, bool, Tensor) -> Tuple[Tensor, Tensor] + terminated = torch.zeros_like(reset_buf) + + if enable_early_termination: + masked_contact_buf = contact_buf.clone() + masked_contact_buf[:, contact_body_ids, :] = 0 + fall_contact = torch.any(torch.abs(masked_contact_buf) > 0.1, dim=-1) + fall_contact = torch.any(fall_contact, dim=-1) + + body_height = rigid_body_pos[..., 2] + fall_height = body_height < termination_heights + fall_height[:, contact_body_ids] = False + fall_height = torch.any(fall_height, dim=-1) + + has_fallen = torch.logical_and(fall_contact, fall_height) + + # first timestep can sometimes still have nonzero contact forces + # so only check after first couple of steps + has_fallen *= progress_buf > 1 + terminated = torch.where(has_fallen, torch.ones_like(reset_buf), terminated) + + reset = torch.where( + progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), terminated + ) + + return reset, terminated diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/__init__.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/base_task.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/base_task.py new file mode 100644 index 000000000..786ff55b3 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/base_task.py @@ -0,0 +1,487 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import sys +import os +import operator +from copy import deepcopy +import random +from gym import spaces + +from isaacgym import gymapi +from isaacgym.gymutil import get_property_setter_map, get_property_getter_map, get_default_setter_args, apply_random_samples, check_buckets, generate_random_samples + +import numpy as np +import torch + + +# Base class for RL tasks +class BaseTask(): + + def __init__(self, cfg, enable_camera_sensors=False): + self.gym = gymapi.acquire_gym() + self.obs_dict = {} + self.device_type = cfg.get("device_type", "cuda") + self.device_id = cfg.get("device_id", 0) + + self.device = "cpu" + if self.device_type == "cuda" or self.device_type == "GPU": + self.device = "cuda" + ":" + str(self.device_id) + + self.headless = cfg["headless"] + + # double check! + self.graphics_device_id = self.device_id + if enable_camera_sensors == False and self.headless == True: + self.graphics_device_id = -1 + + self.num_envs = cfg["env"]["numEnvs"] + self.num_obs = cfg["env"]["numObservations"] + self.num_states = cfg["env"].get("numStates", 0) + self.num_actions = cfg["env"]["numActions"] + + self.control_freq_inv = cfg["env"].get("controlFrequencyInv", 1) + + # optimization flags for pytorch JIT + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + + # allocate buffers + self.obs_buf = torch.zeros( + (self.num_envs, self.num_obs), device=self.device, dtype=torch.float) + self.states_buf = torch.zeros( + (self.num_envs, self.num_states), device=self.device, dtype=torch.float) + self.rew_buf = torch.zeros( + self.num_envs, device=self.device, dtype=torch.float) + self.reset_buf = torch.ones( + self.num_envs, device=self.device, dtype=torch.long) + self.progress_buf = torch.zeros( + self.num_envs, device=self.device, dtype=torch.long) + self.randomize_buf = torch.zeros( + self.num_envs, device=self.device, dtype=torch.long) + self.extras = {} + + self.obs_space = spaces.Box( + np.ones(self.num_obs, dtype=np.float32) * -np.Inf, + np.ones(self.num_obs, dtype=np.float32) * np.Inf, + ) + self.state_space = spaces.Box( + np.ones(self.num_states, dtype=np.float32) * -np.Inf, + np.ones(self.num_states, dtype=np.float32) * np.Inf, + ) + + self.act_space = spaces.Box( + np.ones(self.num_actions, dtype=np.float32) * -1.0, + np.ones(self.num_actions, dtype=np.float32) * 1.0, + ) + + + self.original_props = {} + self.dr_randomizations = {} + self.first_randomization = True + self.actor_params_generator = None + self.extern_actor_params = {} + for env_id in range(self.num_envs): + self.extern_actor_params[env_id] = None + + self.last_step = -1 + self.last_rand_step = -1 + + # create envs, sim and viewer + self.create_sim() + self.gym.prepare_sim(self.sim) + + # todo: read from config + self.enable_viewer_sync = True + self.viewer = None + + # if running with a viewer, set up keyboard shortcuts and camera + if self.headless == False: + # subscribe to keyboard shortcuts + self.viewer = self.gym.create_viewer( + self.sim, gymapi.CameraProperties()) + self.gym.subscribe_viewer_keyboard_event( + self.viewer, gymapi.KEY_ESCAPE, "QUIT") + self.gym.subscribe_viewer_keyboard_event( + self.viewer, gymapi.KEY_V, "toggle_viewer_sync") + + # set the camera position based on up axis + sim_params = self.gym.get_sim_params(self.sim) + if sim_params.up_axis == gymapi.UP_AXIS_Z: + cam_pos = gymapi.Vec3(20.0, 25.0, 3.0) + cam_target = gymapi.Vec3(10.0, 15.0, 0.0) + else: + cam_pos = gymapi.Vec3(20.0, 3.0, 25.0) + cam_target = gymapi.Vec3(10.0, 0.0, 15.0) + + self.gym.viewer_camera_look_at( + self.viewer, None, cam_pos, cam_target) + + + def reset_done(self): + """Reset the environment. + Returns: + Observation dictionary, indices of environments being reset + """ + done_env_ids = self.reset_buf.nonzero(as_tuple=False).flatten() + if len(done_env_ids) > 0: + self.reset_idx(done_env_ids) + + self.obs_dict["obs"] = torch.clamp( + self.obs_buf, -np.Inf, np.Inf + ).to(self.rl_device) + + # asymmetric actor-critic + if self.num_states > 0: + self.obs_dict["states"] = self.get_state() + return self.obs_dict, done_env_ids + + @property + def observation_space(self): + """Get the environment's observation space.""" + return self.obs_space + + @property + def action_space(self): + """Get the environment's action space.""" + return self.act_space + + # set gravity based on up axis and return axis index + def set_sim_params_up_axis(self, sim_params, axis): + if axis == 'z': + sim_params.up_axis = gymapi.UP_AXIS_Z + sim_params.gravity.x = 0 + sim_params.gravity.y = 0 + sim_params.gravity.z = -9.81 + return 2 + return 1 + + def create_sim(self, compute_device, graphics_device, physics_engine, sim_params): + sim = self.gym.create_sim(compute_device, graphics_device, physics_engine, sim_params) + if sim is None: + print("*** Failed to create sim") + quit() + + return sim + + def step(self, actions): + if self.dr_randomizations.get('actions', None): + actions = self.dr_randomizations['actions']['noise_lambda'](actions) + + # apply actions + self.pre_physics_step(actions) + + # step physics and render each frame + self._physics_step() + + # to fix! + if self.device == 'cpu': + self.gym.fetch_results(self.sim, True) + + # compute observations, rewards, resets, ... + self.post_physics_step() + + if self.dr_randomizations.get('observations', None): + self.obs_buf = self.dr_randomizations['observations']['noise_lambda'](self.obs_buf) + + self.obs_dict["obs"] = torch.clamp( + self.obs_buf, -np.Inf, np.Inf + ).to(self.rl_device) + + + return ( + self.obs_dict, + self.rew_buf.to(self.rl_device), + self.reset_buf.to(self.rl_device), + self.extras, + ) + + + + def get_states(self): + return self.states_buf + + def render(self, sync_frame_time=False): + if self.viewer: + # check for window closed + if self.gym.query_viewer_has_closed(self.viewer): + sys.exit() + + # check for keyboard events + for evt in self.gym.query_viewer_action_events(self.viewer): + if evt.action == "QUIT" and evt.value > 0: + sys.exit() + elif evt.action == "toggle_viewer_sync" and evt.value > 0: + self.enable_viewer_sync = not self.enable_viewer_sync + + # fetch results + if self.device != 'cpu': + self.gym.fetch_results(self.sim, True) + + # step graphics + if self.enable_viewer_sync: + self.gym.step_graphics(self.sim) + self.gym.draw_viewer(self.viewer, self.sim, True) + else: + self.gym.poll_viewer_events(self.viewer) + + def get_actor_params_info(self, dr_params, env): + """Returns a flat array of actor params, their names and ranges.""" + if "actor_params" not in dr_params: + return None + params = [] + names = [] + lows = [] + highs = [] + param_getters_map = get_property_getter_map(self.gym) + for actor, actor_properties in dr_params["actor_params"].items(): + handle = self.gym.find_actor_handle(env, actor) + for prop_name, prop_attrs in actor_properties.items(): + if prop_name == 'color': + continue # this is set randomly + props = param_getters_map[prop_name](env, handle) + if not isinstance(props, list): + props = [props] + for prop_idx, prop in enumerate(props): + for attr, attr_randomization_params in prop_attrs.items(): + name = prop_name+'_'+str(prop_idx)+'_'+attr + lo_hi = attr_randomization_params['range'] + distr = attr_randomization_params['distribution'] + if 'uniform' not in distr: + lo_hi = (-1.0*float('Inf'), float('Inf')) + if isinstance(prop, np.ndarray): + for attr_idx in range(prop[attr].shape[0]): + params.append(prop[attr][attr_idx]) + names.append(name+'_'+str(attr_idx)) + lows.append(lo_hi[0]) + highs.append(lo_hi[1]) + else: + params.append(getattr(prop, attr)) + names.append(name) + lows.append(lo_hi[0]) + highs.append(lo_hi[1]) + return params, names, lows, highs + + # Apply randomizations only on resets, due to current PhysX limitations + def apply_randomizations(self, dr_params): + # If we don't have a randomization frequency, randomize every step + rand_freq = dr_params.get("frequency", 1) + + # First, determine what to randomize: + # - non-environment parameters when > frequency steps have passed since the last non-environment + # - physical environments in the reset buffer, which have exceeded the randomization frequency threshold + # - on the first call, randomize everything + self.last_step = self.gym.get_frame_count(self.sim) + if self.first_randomization: + do_nonenv_randomize = True + env_ids = list(range(self.num_envs)) + else: + do_nonenv_randomize = (self.last_step - self.last_rand_step) >= rand_freq + rand_envs = torch.where(self.randomize_buf >= rand_freq, torch.ones_like(self.randomize_buf), torch.zeros_like(self.randomize_buf)) + rand_envs = torch.logical_and(rand_envs, self.reset_buf) + env_ids = torch.nonzero(rand_envs, as_tuple=False).squeeze(-1).tolist() + self.randomize_buf[rand_envs] = 0 + + if do_nonenv_randomize: + self.last_rand_step = self.last_step + + param_setters_map = get_property_setter_map(self.gym) + param_setter_defaults_map = get_default_setter_args(self.gym) + param_getters_map = get_property_getter_map(self.gym) + + # On first iteration, check the number of buckets + if self.first_randomization: + check_buckets(self.gym, self.envs, dr_params) + + for nonphysical_param in ["observations", "actions"]: + if nonphysical_param in dr_params and do_nonenv_randomize: + dist = dr_params[nonphysical_param]["distribution"] + op_type = dr_params[nonphysical_param]["operation"] + sched_type = dr_params[nonphysical_param]["schedule"] if "schedule" in dr_params[nonphysical_param] else None + sched_step = dr_params[nonphysical_param]["schedule_steps"] if "schedule" in dr_params[nonphysical_param] else None + op = operator.add if op_type == 'additive' else operator.mul + + if sched_type == 'linear': + sched_scaling = 1.0 / sched_step * \ + min(self.last_step, sched_step) + elif sched_type == 'constant': + sched_scaling = 0 if self.last_step < sched_step else 1 + else: + sched_scaling = 1 + + if dist == 'gaussian': + mu, var = dr_params[nonphysical_param]["range"] + mu_corr, var_corr = dr_params[nonphysical_param].get("range_correlated", [0., 0.]) + + if op_type == 'additive': + mu *= sched_scaling + var *= sched_scaling + mu_corr *= sched_scaling + var_corr *= sched_scaling + elif op_type == 'scaling': + var = var * sched_scaling # scale up var over time + mu = mu * sched_scaling + 1.0 * \ + (1.0 - sched_scaling) # linearly interpolate + + var_corr = var_corr * sched_scaling # scale up var over time + mu_corr = mu_corr * sched_scaling + 1.0 * \ + (1.0 - sched_scaling) # linearly interpolate + + def noise_lambda(tensor, param_name=nonphysical_param): + params = self.dr_randomizations[param_name] + corr = params.get('corr', None) + if corr is None: + corr = torch.randn_like(tensor) + params['corr'] = corr + corr = corr * params['var_corr'] + params['mu_corr'] + return op( + tensor, corr + torch.randn_like(tensor) * params['var'] + params['mu']) + + self.dr_randomizations[nonphysical_param] = {'mu': mu, 'var': var, 'mu_corr': mu_corr, 'var_corr': var_corr, 'noise_lambda': noise_lambda} + + elif dist == 'uniform': + lo, hi = dr_params[nonphysical_param]["range"] + lo_corr, hi_corr = dr_params[nonphysical_param].get("range_correlated", [0., 0.]) + + if op_type == 'additive': + lo *= sched_scaling + hi *= sched_scaling + lo_corr *= sched_scaling + hi_corr *= sched_scaling + elif op_type == 'scaling': + lo = lo * sched_scaling + 1.0 * (1.0 - sched_scaling) + hi = hi * sched_scaling + 1.0 * (1.0 - sched_scaling) + lo_corr = lo_corr * sched_scaling + 1.0 * (1.0 - sched_scaling) + hi_corr = hi_corr * sched_scaling + 1.0 * (1.0 - sched_scaling) + + def noise_lambda(tensor, param_name=nonphysical_param): + params = self.dr_randomizations[param_name] + corr = params.get('corr', None) + if corr is None: + corr = torch.randn_like(tensor) + params['corr'] = corr + corr = corr * (params['hi_corr'] - params['lo_corr']) + params['lo_corr'] + return op(tensor, corr + torch.rand_like(tensor) * (params['hi'] - params['lo']) + params['lo']) + + self.dr_randomizations[nonphysical_param] = {'lo': lo, 'hi': hi, 'lo_corr': lo_corr, 'hi_corr': hi_corr, 'noise_lambda': noise_lambda} + + if "sim_params" in dr_params and do_nonenv_randomize: + prop_attrs = dr_params["sim_params"] + prop = self.gym.get_sim_params(self.sim) + + if self.first_randomization: + self.original_props["sim_params"] = { + attr: getattr(prop, attr) for attr in dir(prop)} + + for attr, attr_randomization_params in prop_attrs.items(): + apply_random_samples( + prop, self.original_props["sim_params"], attr, attr_randomization_params, self.last_step) + + self.gym.set_sim_params(self.sim, prop) + + # If self.actor_params_generator is initialized: use it to + # sample actor simulation params. This gives users the + # freedom to generate samples from arbitrary distributions, + # e.g. use full-covariance distributions instead of the DR's + # default of treating each simulation parameter independently. + extern_offsets = {} + if self.actor_params_generator is not None: + for env_id in env_ids: + self.extern_actor_params[env_id] = \ + self.actor_params_generator.sample() + extern_offsets[env_id] = 0 + + for actor, actor_properties in dr_params["actor_params"].items(): + for env_id in env_ids: + env = self.envs[env_id] + handle = self.gym.find_actor_handle(env, actor) + extern_sample = self.extern_actor_params[env_id] + + for prop_name, prop_attrs in actor_properties.items(): + if prop_name == 'color': + num_bodies = self.gym.get_actor_rigid_body_count( + env, handle) + for n in range(num_bodies): + self.gym.set_rigid_body_color(env, handle, n, gymapi.MESH_VISUAL, + gymapi.Vec3(random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1))) + continue + if prop_name == 'scale': + attr_randomization_params = prop_attrs + sample = generate_random_samples(attr_randomization_params, 1, + self.last_step, None) + og_scale = 1 + if attr_randomization_params['operation'] == 'scaling': + new_scale = og_scale * sample + elif attr_randomization_params['operation'] == 'additive': + new_scale = og_scale + sample + self.gym.set_actor_scale(env, handle, new_scale) + continue + + prop = param_getters_map[prop_name](env, handle) + if isinstance(prop, list): + if self.first_randomization: + self.original_props[prop_name] = [ + {attr: getattr(p, attr) for attr in dir(p)} for p in prop] + for p, og_p in zip(prop, self.original_props[prop_name]): + for attr, attr_randomization_params in prop_attrs.items(): + smpl = None + if self.actor_params_generator is not None: + smpl, extern_offsets[env_id] = get_attr_val_from_sample( + extern_sample, extern_offsets[env_id], p, attr) + apply_random_samples( + p, og_p, attr, attr_randomization_params, + self.last_step, smpl) + else: + if self.first_randomization: + self.original_props[prop_name] = deepcopy(prop) + for attr, attr_randomization_params in prop_attrs.items(): + smpl = None + if self.actor_params_generator is not None: + smpl, extern_offsets[env_id] = get_attr_val_from_sample( + extern_sample, extern_offsets[env_id], prop, attr) + apply_random_samples( + prop, self.original_props[prop_name], attr, + attr_randomization_params, self.last_step, smpl) + + setter = param_setters_map[prop_name] + default_args = param_setter_defaults_map[prop_name] + setter(env, handle, prop, *default_args) + + if self.actor_params_generator is not None: + for env_id in env_ids: # check that we used all dims in sample + if extern_offsets[env_id] > 0: + extern_sample = self.extern_actor_params[env_id] + if extern_offsets[env_id] != extern_sample.shape[0]: + print('env_id', env_id, + 'extern_offset', extern_offsets[env_id], + 'vs extern_sample.shape', extern_sample.shape) + raise Exception("Invalid extern_sample size") + + self.first_randomization = False + + def pre_physics_step(self, actions): + raise NotImplementedError + + def _physics_step(self): + for i in range(self.control_freq_inv): + self.render() + self.gym.simulate(self.sim) + return + + def post_physics_step(self): + raise NotImplementedError + + +def get_attr_val_from_sample(sample, offset, prop, attr): + """Retrieves param value for the given prop and attr from the sample.""" + if sample is None: + return None, 0 + if isinstance(prop, np.ndarray): + smpl = sample[offset:offset+prop[attr].shape[0]] + return smpl, offset+prop[attr].shape[0] + else: + return sample[offset], offset+1 diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/humanoid_physhoi.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/humanoid_physhoi.py new file mode 100644 index 000000000..9ef8d2af2 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/humanoid_physhoi.py @@ -0,0 +1,1480 @@ +# Copyright 2023, Junjia LIU, jjliu@mae.cuhk.edu.hk +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import random +from enum import Enum + +from isaacgym import gymapi +from isaacgym import gymtorch +from isaacgym.torch_utils import * + +import rofunc as rf +from rofunc.learning.RofuncRL.tasks.isaacgymenv.base.vec_task import VecTask +from rofunc.learning.RofuncRL.tasks.utils import torch_jit_utils as torch_utils +from rofunc.utils.oslab import get_rofunc_path + +PERTURB_OBJS = [ + ["small", 60], + # ["large", 60], +] + + +class Humanoid_SMPLX(VecTask): + def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render): + + self.cfg = cfg + + self._pd_control = self.cfg["env"]["pdControl"] + self.power_scale = self.cfg["env"]["powerScale"] + + self.debug_viz = self.cfg["env"]["enableDebugVis"] + self.plane_static_friction = self.cfg["env"]["plane"]["staticFriction"] + self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"] + self.plane_restitution = self.cfg["env"]["plane"]["restitution"] + + self.ref_hoi_obs_size = 324 + len(self.cfg["env"]["keyBodies"]) * 3 + self._local_root_obs = self.cfg["env"]["localRootObs"] + self._root_height_obs = self.cfg["env"].get("rootHeightObs", True) + self._enable_early_termination = self.cfg["env"]["enableEarlyTermination"] + self.camera_follow = self.cfg["env"].get("cameraFollow", False) + + # self.max_episode_length = self.cfg["env"]["episodeLength"] + + key_bodies = self.cfg["env"]["keyBodies"] + self._setup_character_props(key_bodies) + + self.cfg["env"]["numObservations"] = self.get_obs_size() + self.cfg["env"]["numActions"] = self.get_action_size() + + # self.cfg["device_type"] = device_type + # self.cfg["device_id"] = device_id + # self.cfg["headless"] = headless + + # super().__init__(cfg=self.cfg) + super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, + graphics_device_id=graphics_device_id, headless=headless, + virtual_screen_capture=virtual_screen_capture, force_render=force_render) + + dt = self.cfg["sim"]["dt"] + self.dt = self.control_freq_inv * dt + + # get gym GPU state tensors + actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim) + dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim) + sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim) + rigid_body_state = self.gym.acquire_rigid_body_state_tensor(self.sim) + contact_force_tensor = self.gym.acquire_net_contact_force_tensor(self.sim) + + # sensors_per_env = 2 + # self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, sensors_per_env * 6) + + dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim) + self.dof_force_tensor = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs, self.num_dof) + + self.gym.refresh_dof_state_tensor(self.sim) + self.gym.refresh_actor_root_state_tensor(self.sim) + self.gym.refresh_rigid_body_state_tensor(self.sim) + self.gym.refresh_net_contact_force_tensor(self.sim) + + self._root_states = gymtorch.wrap_tensor(actor_root_state) + num_actors = self.get_num_actors_per_env() + + self._humanoid_root_states = self._root_states.view( + self.num_envs, num_actors, actor_root_state.shape[-1])[..., 0, :] + self._initial_humanoid_root_states = self._humanoid_root_states.clone() + self._initial_humanoid_root_states[:, 7:13] = 0 + + self._humanoid_actor_ids = num_actors * torch.arange(self.num_envs, device=self.device, dtype=torch.int32) + + # create some wrapper tensors for different slices + self._dof_state = gymtorch.wrap_tensor(dof_state_tensor) + dofs_per_env = self._dof_state.shape[0] // self.num_envs + self._dof_pos = self._dof_state.view(self.num_envs, dofs_per_env, 2)[..., :self.num_dof, 0] + self._dof_vel = self._dof_state.view(self.num_envs, dofs_per_env, 2)[..., :self.num_dof, 1] + + self._initial_dof_pos = torch.zeros_like(self._dof_pos, device=self.device, dtype=torch.float) + self._initial_dof_vel = torch.zeros_like(self._dof_vel, device=self.device, dtype=torch.float) + + self._rigid_body_state = gymtorch.wrap_tensor(rigid_body_state) + bodies_per_env = self._rigid_body_state.shape[0] // self.num_envs + rigid_body_state_reshaped = self._rigid_body_state.view(self.num_envs, bodies_per_env, 13) + + self._rigid_body_pos = rigid_body_state_reshaped[..., :self.num_bodies, 0:3] + self._rigid_body_rot = rigid_body_state_reshaped[..., :self.num_bodies, 3:7] + self._rigid_body_vel = rigid_body_state_reshaped[..., :self.num_bodies, 7:10] + self._rigid_body_ang_vel = rigid_body_state_reshaped[..., :self.num_bodies, 10:13] + + contact_force_tensor = gymtorch.wrap_tensor(contact_force_tensor) + self._contact_forces = contact_force_tensor.view(self.num_envs, bodies_per_env, 3)[..., :self.num_bodies, :] + + self._terminate_buf = torch.ones(self.num_envs, device=self.device, dtype=torch.long) + + self._build_termination_heights() + + contact_bodies = self.cfg["env"]["contactBodies"] + self._key_body_ids = self._build_key_body_ids_tensor(key_bodies) + self._contact_body_ids = self._build_contact_body_ids_tensor(contact_bodies) + + if self.viewer != None: + self._init_camera() + + def get_obs_size(self): + return self._num_obs + + def get_action_size(self): + return self._num_actions + + def get_num_actors_per_env(self): + num_actors = self._root_states.shape[0] // self.num_envs + return num_actors + + def create_sim(self): + self.up_axis_idx = 2 # index of up axis: Y=1, Z=2 + self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params) + + self._create_ground_plane() + self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs))) + + # def reset(self, env_ids=None): + # if (env_ids is None): + # env_ids = to_torch(np.arange(self.num_envs), device=self.device, dtype=torch.long) + # self.reset_idx(env_ids) + + def reset(self): + actions = 0.01 * (1 - 2 * np.random.rand(self.num_envs, self.num_actions)).astype('f') + actions = to_torch(actions, device=self.rl_device, dtype=torch.float) + + # step the simulator + obs, rewards, resets, extras = self.step(actions) + + return torch.clamp(obs["obs"], -self.clip_obs, self.clip_obs) + + def set_char_color(self, col, env_ids): + for env_id in env_ids: + env_ptr = self.envs[env_id] + handle = self.humanoid_handles[env_id] + + for j in range(self.num_bodies): + self.gym.set_rigid_body_color(env_ptr, handle, j, gymapi.MESH_VISUAL, + gymapi.Vec3(col[0], col[1], col[2])) + + def reset_idx(self, env_ids): + if len(env_ids) > 0: + self._reset_actors(env_ids) + self._reset_env_tensors(env_ids) + self._refresh_sim_tensors() + self._compute_observations(env_ids) + + def _reset_env_tensors(self, env_ids): + env_ids_int32 = self._humanoid_actor_ids[env_ids] + self.gym.set_actor_root_state_tensor_indexed(self.sim, + gymtorch.unwrap_tensor(self._root_states), + gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32)) + self.gym.set_dof_state_tensor_indexed(self.sim, + gymtorch.unwrap_tensor(self._dof_state), + gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32)) + self.progress_buf[env_ids] = self.motion_times.clone() + self.reset_buf[env_ids] = 0 + self._terminate_buf[env_ids] = 0 + + def _create_ground_plane(self): + plane_params = gymapi.PlaneParams() + plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0) + plane_params.static_friction = self.plane_static_friction + plane_params.dynamic_friction = self.plane_dynamic_friction + plane_params.restitution = self.plane_restitution + self.gym.add_ground(self.sim, plane_params) + + def _setup_character_props(self, key_bodies): + asset_file = self.cfg["env"]["asset"]["assetFileName"] + num_key_bodies = len(key_bodies) + + if (asset_file == "smplx/smplx_capsule.xml"): + self._dof_obs_size = (51) * 3 + self._num_actions = (51) * 3 + obj_obs_size = 15 + self._num_obs = 1 + (52) * (3 + 6 + 3 + 3) - 3 + 10 * 3 + obj_obs_size + self.ref_hoi_obs_size + else: + raise rf.logger.beauty_print(f"Unsupported character config file: {asset_file}") + + def _build_termination_heights(self): + self._termination_heights = 0.3 + self._termination_heights = to_torch(self._termination_heights, device=self.device) + + def get_num_amp_obs(self): + return self.ref_hoi_obs_size + + def _create_envs(self, num_envs, spacing, num_per_row): + lower = gymapi.Vec3(-spacing, -spacing, 0.0) + upper = gymapi.Vec3(spacing, spacing, spacing) + + # get rofunc path from rofunc package metadata + rofunc_path = get_rofunc_path() + asset_root = os.path.join(rofunc_path, "simulator/assets") + + # Load humanoid asset + asset_file = self.cfg["env"]["asset"]["assetFileName"] + + asset_path = os.path.join(asset_root, asset_file) + asset_root = os.path.dirname(asset_path) + asset_file = os.path.basename(asset_path) + + asset_options = gymapi.AssetOptions() + asset_options.angular_damping = 0.01 + asset_options.max_angular_velocity = 100.0 + asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE + # asset_options.fix_base_link = True + # asset_options.disable_gravity = True + humanoid_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options) + + self.num_humanoid_bodies = self.gym.get_asset_rigid_body_count(humanoid_asset) + self.num_humanoid_shapes = self.gym.get_asset_rigid_shape_count(humanoid_asset) + + actuator_props = self.gym.get_asset_actuator_properties(humanoid_asset) + motor_efforts = [prop.motor_effort for prop in actuator_props] + + # create force sensors at the feet + right_foot_idx = self.gym.find_asset_rigid_body_index(humanoid_asset, "right_foot") + left_foot_idx = self.gym.find_asset_rigid_body_index(humanoid_asset, "left_foot") + sensor_pose = gymapi.Transform() + + self.gym.create_asset_force_sensor(humanoid_asset, right_foot_idx, sensor_pose) + self.gym.create_asset_force_sensor(humanoid_asset, left_foot_idx, sensor_pose) + + self.max_motor_effort = max(motor_efforts) + self.motor_efforts = to_torch(motor_efforts, device=self.device) + + self.torso_index = 0 + self.num_bodies = self.gym.get_asset_rigid_body_count(humanoid_asset) + self.num_dof = self.gym.get_asset_dof_count(humanoid_asset) + self.num_joints = self.gym.get_asset_joint_count(humanoid_asset) + + self.humanoid_handles = [] + self.envs = [] + self.dof_limits_lower = [] + self.dof_limits_upper = [] + + max_agg_bodies = self.num_humanoid_bodies + 2 + max_agg_shapes = self.num_humanoid_shapes + 2 + + for i in range(self.num_envs): + # create env instance + env_ptr = self.gym.create_env(self.sim, lower, upper, num_per_row) + self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True) + + self._build_env(i, env_ptr, humanoid_asset) + + self.gym.end_aggregate(env_ptr) + self.envs.append(env_ptr) + + dof_prop = self.gym.get_actor_dof_properties(self.envs[0], self.humanoid_handles[0]) + for j in range(self.num_dof): + if dof_prop["lower"][j] > dof_prop["upper"][j]: + self.dof_limits_lower.append(dof_prop["upper"][j]) + self.dof_limits_upper.append(dof_prop["lower"][j]) + else: + self.dof_limits_lower.append(dof_prop["lower"][j]) + self.dof_limits_upper.append(dof_prop["upper"][j]) + + self.dof_limits_lower = to_torch(self.dof_limits_lower, device=self.device) + self.dof_limits_upper = to_torch(self.dof_limits_upper, device=self.device) + + if self._pd_control: + self._build_pd_action_offset_scale() + + def _build_env(self, env_id, env_ptr, humanoid_asset): + col_group = env_id + col_filter = self._get_humanoid_collision_filter() + segmentation_id = 0 + + start_pose = gymapi.Transform() + char_h = 0.89 + + start_pose.p = gymapi.Vec3(*get_axis_params(char_h, self.up_axis_idx)) + start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0) + + humanoid_handle = self.gym.create_actor( + env_ptr, + humanoid_asset, + start_pose, + "humanoid", + col_group, + col_filter, + segmentation_id, + ) + + self.gym.enable_actor_dof_force_sensors(env_ptr, humanoid_handle) + + for j in range(self.num_bodies): + self.gym.set_rigid_body_color( + env_ptr, + humanoid_handle, + j, + gymapi.MESH_VISUAL, + gymapi.Vec3(0.54, 0.85, 0.2), + ) + + if self._pd_control: + dof_prop = self.gym.get_asset_dof_properties(humanoid_asset) + dof_prop["driveMode"] = gymapi.DOF_MODE_POS + self.gym.set_actor_dof_properties(env_ptr, humanoid_handle, dof_prop) + + self.humanoid_handles.append(humanoid_handle) + + def _build_pd_action_offset_scale(self): + + lim_low = self.dof_limits_lower.cpu().numpy() + lim_high = self.dof_limits_upper.cpu().numpy() + + self._pd_action_offset = 0.5 * (lim_high + lim_low) + self._pd_action_scale = 0.5 * (lim_high - lim_low) + self._pd_action_offset = to_torch(self._pd_action_offset, device=self.device) + self._pd_action_scale = to_torch(self._pd_action_scale, device=self.device) + + def _get_humanoid_collision_filter(self): + return 0 + + def _compute_reward(self, actions): + self.rew_buf[:] = compute_humanoid_reward( + self._curr_ref_obs, + self._curr_obs, + self._contact_forces, + self._tar_contact_forces, + len(self._key_body_ids), + self.reward_weights_p, + self.reward_weights_r, + self.reward_weights_pv, + self.reward_weights_rv, + self.reward_weights_op, + self.reward_weights_or, + self.reward_weights_opv, + self.reward_weights_orv, + self.reward_weights_ig, + self.reward_weights_cg1, + self.reward_weights_cg2, + ) + + def _compute_reset(self): + self.reset_buf[:], self._terminate_buf[:] = compute_humanoid_reset(self.reset_buf, self.progress_buf, + self._contact_forces, + self._rigid_body_pos, + self.max_episode_length, + self._enable_early_termination, + self._termination_heights, + self._curr_ref_obs, self._curr_obs, + ) + + def _refresh_sim_tensors(self): + self.gym.refresh_dof_state_tensor(self.sim) + self.gym.refresh_actor_root_state_tensor(self.sim) + self.gym.refresh_rigid_body_state_tensor(self.sim) + + self.gym.refresh_force_sensor_tensor(self.sim) + self.gym.refresh_dof_force_tensor(self.sim) + self.gym.refresh_net_contact_force_tensor(self.sim) + + def _compute_task_obs(self, env_ids=None): + if (env_ids is None): + root_states = self._humanoid_root_states + tar_states = self._target_states + else: + root_states = self._humanoid_root_states[env_ids] + tar_states = self._target_states[env_ids] + + obs = compute_obj_observations(root_states, tar_states) + return obs + + def _compute_observations(self, env_ids=None): + obs = self._compute_humanoid_obs(env_ids) + + task_obs = self._compute_task_obs(env_ids) + obs = torch.cat([obs, task_obs], dim=-1) + + if env_ids is None: + ts = self.progress_buf.clone() + + self._curr_ref_obs = self.hoi_data_dict[0]['hoi_data'][ts].clone() + next_ts = torch.clamp(ts + 1, max=self.max_episode_length - 1) + ref_obs = self.hoi_data_dict[0]['hoi_data'][next_ts].clone() + self.obs_buf[:] = torch.cat((obs, ref_obs), dim=-1) + + else: + ts = self.progress_buf[env_ids].clone() + self._curr_ref_obs[env_ids] = self.hoi_data_dict[0]['hoi_data'][ts].clone() + next_ts = torch.clamp(ts + 1, max=self.max_episode_length - 1) + ref_obs = self.hoi_data_dict[0]['hoi_data'][next_ts].clone() + self.obs_buf[env_ids] = torch.cat((obs, ref_obs), dim=-1) + + def _compute_humanoid_obs(self, env_ids=None): + if env_ids is None: + body_pos = self._rigid_body_pos + body_rot = self._rigid_body_rot + body_vel = self._rigid_body_vel + body_ang_vel = self._rigid_body_ang_vel + contact_forces = self._contact_forces + else: + body_pos = self._rigid_body_pos[env_ids] + body_rot = self._rigid_body_rot[env_ids] + body_vel = self._rigid_body_vel[env_ids] + body_ang_vel = self._rigid_body_ang_vel[env_ids] + contact_forces = self._contact_forces[env_ids] + + obs = compute_humanoid_observations_max(body_pos, body_rot, body_vel, body_ang_vel, self._local_root_obs, + self._root_height_obs, + contact_forces, self._contact_body_ids) + + return obs + + def _reset_actors(self, env_ids): + self._humanoid_root_states[env_ids] = self._initial_humanoid_root_states[env_ids] + self._dof_pos[env_ids] = self._initial_dof_pos[env_ids] + self._dof_vel[env_ids] = self._initial_dof_vel[env_ids] + + def pre_physics_step(self, actions): + self.actions = actions.to(self.device).clone() + + if self._pd_control: + pd_tar = self._action_to_pd_targets(self.actions) + pd_tar_tensor = gymtorch.unwrap_tensor(pd_tar) + self.gym.set_dof_position_target_tensor(self.sim, pd_tar_tensor) + else: + forces = self.actions * self.motor_efforts.unsqueeze(0) * self.power_scale + force_tensor = gymtorch.unwrap_tensor(forces) + self.gym.set_dof_actuation_force_tensor(self.sim, force_tensor) + + def post_physics_step(self): + self.progress_buf += 1 + + self._refresh_sim_tensors() + + # env_ids = self.reset_buf.nonzero(as_tuple=False).flatten() + # if len(env_ids) > 0: + # self.reset_idx(env_ids) + + # extra calc of self._curr_hoi_obs_buf, for correct calculate of imitation reward + self._compute_hoi_observations() + + self._compute_observations() + self._compute_reward(self.actions) + self._compute_reset() + + self.extras["terminate"] = self._terminate_buf + + # debug viz + if self.viewer and self.debug_viz: + self._update_debug_viz() + + def render(self, sync_frame_time=False): + if self.viewer and self.camera_follow: + self._update_camera() + + super().render(sync_frame_time) + + def _build_key_body_ids_tensor(self, key_body_names): + env_ptr = self.envs[0] + actor_handle = self.humanoid_handles[0] + body_ids = [] + + for body_name in key_body_names: + body_id = self.gym.find_actor_rigid_body_handle( + env_ptr, actor_handle, body_name + ) + assert body_id != -1 + body_ids.append(body_id) + + body_ids = to_torch(body_ids, device=self.device, dtype=torch.long) + return body_ids + + def _build_contact_body_ids_tensor(self, contact_body_names): + env_ptr = self.envs[0] + actor_handle = self.humanoid_handles[0] + body_ids = [] + + for body_name in contact_body_names: + body_id = self.gym.find_actor_rigid_body_handle( + env_ptr, actor_handle, body_name + ) + assert body_id != -1 + body_ids.append(body_id) + + body_ids = to_torch(body_ids, device=self.device, dtype=torch.long) + return body_ids + + def _action_to_pd_targets(self, action): + pd_tar = self._pd_action_offset + self._pd_action_scale * action + return pd_tar + + def _init_camera(self): + self.gym.refresh_actor_root_state_tensor(self.sim) + self._cam_prev_char_pos = self._humanoid_root_states[0, 0:3].cpu().numpy() + + cam_pos = gymapi.Vec3(self._cam_prev_char_pos[0], + self._cam_prev_char_pos[1] - 3.0, + 1.0) + cam_target = gymapi.Vec3(self._cam_prev_char_pos[0], + self._cam_prev_char_pos[1], + 1.0) + self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target) + + def _update_camera(self): + self.gym.refresh_actor_root_state_tensor(self.sim) + char_root_pos = self._humanoid_root_states[0, 0:3].cpu().numpy() + + cam_trans = self.gym.get_viewer_camera_transform(self.viewer, None) + cam_pos = np.array([cam_trans.p.x, cam_trans.p.y, cam_trans.p.z]) + cam_delta = cam_pos - self._cam_prev_char_pos + + new_cam_target = gymapi.Vec3(char_root_pos[0], char_root_pos[1], 1.0) + new_cam_pos = gymapi.Vec3(char_root_pos[0] + cam_delta[0], + char_root_pos[1] + cam_delta[1], + cam_pos[2]) + + self.gym.viewer_camera_look_at(self.viewer, None, new_cam_pos, new_cam_target) + + self._cam_prev_char_pos[:] = char_root_pos + + # # # fixed camera + # new_cam_target = gymapi.Vec3(0, 0.5, 1.0) + # new_cam_pos = gymapi.Vec3(1, -1, 1.6) + # self.gym.viewer_camera_look_at(self.viewer, None, new_cam_pos, new_cam_target) + + def _update_debug_viz(self): + self.gym.clear_lines(self.viewer) + + +class HumanoidPhysHOITask(Humanoid_SMPLX): + class StateInit(Enum): + Default = 0 + Start = 1 + Random = 2 + Hybrid = 3 + + def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render): + + state_init = cfg["env"]["stateInit"] + self._state_init = HumanoidPhysHOITask.StateInit[state_init] + self._hybrid_init_prob = cfg["env"]["hybridInitProb"] + + self._reset_default_env_ids = [] + self._reset_ref_env_ids = [] + self.motion_file = cfg['env']['motion_file'] + self.play_dataset = cfg['env']['playdataset'] + self.projtype = cfg['env']['projtype'] + self.robot_type = cfg["env"]["asset"]["assetFileName"] + self.reward_weights = cfg["env"]["rewardWeights"] + self.reward_weights_p = cfg["env"]["rewardWeights"]["p"] + self.reward_weights_r = cfg["env"]["rewardWeights"]["r"] + self.reward_weights_pv = cfg["env"]["rewardWeights"]["pv"] + self.reward_weights_rv = cfg["env"]["rewardWeights"]["rv"] + self.reward_weights_op = cfg["env"]["rewardWeights"]["op"] + self.reward_weights_or = cfg["env"]["rewardWeights"]["or"] + self.reward_weights_opv = cfg["env"]["rewardWeights"]["opv"] + self.reward_weights_orv = cfg["env"]["rewardWeights"]["orv"] + self.reward_weights_ig = cfg["env"]["rewardWeights"]["ig"] + self.reward_weights_cg1 = cfg["env"]["rewardWeights"]["cg1"] + self.reward_weights_cg2 = cfg["env"]["rewardWeights"]["cg2"] + + self.save_images = cfg['env']['saveImages'] + self.init_vel = cfg['env']['initVel'] + self.ball_size = cfg['env']['ballSize'] + + super().__init__(cfg=cfg, + rl_device=rl_device, + sim_device=sim_device, + graphics_device_id=graphics_device_id, + headless=headless, + virtual_screen_capture=virtual_screen_capture, + force_render=force_render) + + if rf.oslab.is_absl_path(self.motion_file): + motion_file_path = self.motion_file + elif self.motion_file.split("/")[0] == "examples": + motion_file_path = os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "../../../../../../" + self.motion_file, + ) + else: + raise ValueError("Unsupported motion file path") + self.motion_file = motion_file_path + self._load_motion(self.motion_file) + + self._curr_ref_obs = torch.zeros((self.num_envs, self.ref_hoi_obs_size), device=self.device, dtype=torch.float) + self._hist_ref_obs = torch.zeros((self.num_envs, self.ref_hoi_obs_size), device=self.device, dtype=torch.float) + self._curr_obs = torch.zeros((self.num_envs, self.ref_hoi_obs_size), device=self.device, dtype=torch.float) + self._hist_obs = torch.zeros((self.num_envs, self.ref_hoi_obs_size), device=self.device, dtype=torch.float) + self._tar_pos = torch.zeros([self.num_envs, 3], device=self.device, dtype=torch.float) + self._build_target_tensors() + # self._build_marker_state_tensors() + + if self.projtype == "Mouse" or self.projtype == "Auto": + self._build_proj_tensors() + + def post_physics_step(self): + if self.projtype == "Mouse" or self.projtype == "Auto": + self._update_proj() + + super().post_physics_step() + + self._update_hist_hoi_obs() + self._compute_hoi_observations() + + def _update_hist_hoi_obs(self, env_ids=None): + self._hist_obs = self._curr_obs.clone() + + def _setup_character_props(self, key_bodies): + super()._setup_character_props(key_bodies) + + def _load_motion(self, motion_file): + + # '''load HOI dataset''' + self.num_motions = 1 + data_path = motion_file + self.hoi_data_dict = {} + + loaded_dict = {} + hoi_data = torch.load(data_path) + loaded_dict['hoi_data'] = hoi_data.detach().to('cuda') + + # '''change the data framerate''' + # NOTE: this is used for temporary testing, and is not rigorous that may yield incorrect rotations. + dataFramesScale = self.cfg["env"]["dataFramesScale"] + scale_hoi_data = torch.nn.functional.interpolate(loaded_dict['hoi_data'].unsqueeze( + 1).transpose(0, 2), scale_factor=dataFramesScale, mode='linear', align_corners=True) + loaded_dict['hoi_data'] = scale_hoi_data.transpose(0, 2).squeeze(1).clone().contiguous() + + self.max_episode_length = loaded_dict['hoi_data'].shape[0] + self.fps_data = 30. + # self.fps_data = self.cfg["env"]["dataFPS"]*dataFramesScale + + loaded_dict['root_pos'] = loaded_dict['hoi_data'][:, 0:3].clone() + loaded_dict['root_pos_vel'] = (loaded_dict['root_pos'][1:, :].clone() - + loaded_dict['root_pos'][:-1, :].clone()) * self.fps_data + loaded_dict['root_pos_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['root_pos_vel'].shape[-1])).to('cuda'), loaded_dict['root_pos_vel']), dim=0) + + loaded_dict['root_rot'] = loaded_dict['hoi_data'][:, 3:6].clone() + loaded_dict['root_rot_data'] = loaded_dict['root_rot'].clone() + loaded_dict['root_rot_vel'] = (loaded_dict['root_rot'][1:, :].clone() - + loaded_dict['root_rot'][:-1, :].clone()) * self.fps_data + loaded_dict['root_rot_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['root_rot_vel'].shape[-1])).to('cuda'), loaded_dict['root_rot_vel']), dim=0) + loaded_dict['root_rot'] = torch_utils.exp_map_to_quat(loaded_dict['root_rot']).clone() + + loaded_dict['dof_pos'] = loaded_dict['hoi_data'][:, 9:9 + 153].clone() + loaded_dict['dof_pos_vel'] = (loaded_dict['dof_pos'][1:, :].clone() - + loaded_dict['dof_pos'][:-1, :].clone()) * self.fps_data + loaded_dict['dof_pos_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['dof_pos_vel'].shape[-1])).to('cuda'), loaded_dict['dof_pos_vel']), dim=0) + + loaded_dict['body_pos'] = loaded_dict['hoi_data'][:, 162: 162 + 52 * 3].clone().view(self.max_episode_length, + 52, 3) + loaded_dict['key_body_pos'] = loaded_dict['body_pos'][:, + self._key_body_ids, :].view(self.max_episode_length, -1).clone() + loaded_dict['key_body_pos_vel'] = (loaded_dict['key_body_pos'][1:, :].clone() - + loaded_dict['key_body_pos'][:-1, :].clone()) * self.fps_data + loaded_dict['key_body_pos_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['key_body_pos_vel'].shape[-1])).to('cuda'), loaded_dict['key_body_pos_vel']), + dim=0) + + loaded_dict['obj_pos'] = loaded_dict['hoi_data'][:, 318:321].clone() + loaded_dict['obj_pos_vel'] = (loaded_dict['obj_pos'][1:, :].clone() - + loaded_dict['obj_pos'][:-1, :].clone()) * self.fps_data + if self.init_vel: + loaded_dict['obj_pos_vel'] = torch.cat((loaded_dict['obj_pos_vel'][:1], loaded_dict['obj_pos_vel']), dim=0) + else: + loaded_dict['obj_pos_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['obj_pos_vel'].shape[-1])).to('cuda'), loaded_dict['obj_pos_vel']), dim=0) + + loaded_dict['obj_rot'] = -loaded_dict['hoi_data'][:, 321:324].clone() + loaded_dict['obj_rot_vel'] = (loaded_dict['obj_rot'][1:, :].clone() - + loaded_dict['obj_rot'][:-1, :].clone()) * self.fps_data + loaded_dict['obj_rot_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['obj_rot_vel'].shape[-1])).to('cuda'), loaded_dict['obj_rot_vel']), dim=0) + loaded_dict['obj_rot'] = torch_utils.exp_map_to_quat(-loaded_dict['hoi_data'][:, 321:324]).clone() + + loaded_dict['contact'] = torch.round(loaded_dict['hoi_data'][:, 330:331].clone()) + + loaded_dict['hoi_data'] = torch.cat(( + loaded_dict['root_pos'].clone(), + loaded_dict['root_rot'].clone(), + loaded_dict['dof_pos'].clone(), + loaded_dict['dof_pos_vel'].clone(), + loaded_dict['obj_pos'].clone(), + loaded_dict['obj_rot'].clone(), + loaded_dict['obj_pos_vel'].clone(), + loaded_dict['key_body_pos'][:, :].clone(), + loaded_dict['contact'].clone() + ), dim=-1) + + assert (self.ref_hoi_obs_size == loaded_dict['hoi_data'].shape[-1]) + + self.hoi_data_dict[0] = loaded_dict + + def _update_marker(self): + + self._marker_states[:, :3] = self.hoi_data_dict[0]['obj_pos'][self.progress_buf, :] # .clone() + self._marker_states[:, 3:7] = self.hoi_data_dict[0]['obj_rot'][self.progress_buf, :] # .clone() #rand_rot + self._marker_states[:, 7:10] = self.hoi_data_dict[0]['obj_pos_vel'][self.progress_buf, :] # .clone() + self._marker_states[:, 10:13] = self.hoi_data_dict[0]['obj_rot_vel'][self.progress_buf, :] # .clone() + + self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._root_states), + gymtorch.unwrap_tensor(self._marker_actor_ids), + len(self._marker_actor_ids)) + + def _create_envs(self, num_envs, spacing, num_per_row): + + self._target_handles = [] + self._load_target_asset() + # self._marker_handles = [] + # self._load_marker_asset() + if self.projtype == "Mouse" or self.projtype == "Auto": + self._proj_handles = [] + self._load_proj_asset() + super()._create_envs(num_envs, spacing, num_per_row) + + def _build_env(self, env_id, env_ptr, humanoid_asset): + super()._build_env(env_id, env_ptr, humanoid_asset) + + self._build_target(env_id, env_ptr) + # self._build_marker(env_id, env_ptr) + if self.projtype == "Mouse" or self.projtype == "Auto": + self._build_proj(env_id, env_ptr) + + def _build_proj(self, env_id, env_ptr): + col_group = env_id + col_filter = 0 + segmentation_id = 0 + + for i, obj in enumerate(PERTURB_OBJS): + default_pose = gymapi.Transform() + default_pose.p.x = 200 + i + default_pose.p.z = 1 + obj_type = obj[0] + if (obj_type == "small"): + proj_asset = self._small_proj_asset + elif (obj_type == "large"): + proj_asset = self._large_proj_asset + + proj_handle = self.gym.create_actor(env_ptr, proj_asset, default_pose, + "proj{:d}".format(i), col_group, col_filter, segmentation_id) + self._proj_handles.append(proj_handle) + self.gym.set_actor_scale(env_ptr, proj_handle, 1) + + def _build_proj_tensors(self): + self._proj_dist_min = 4 + self._proj_dist_max = 5 + self._proj_h_min = 0.25 + self._proj_h_max = 2 + self._proj_steps = 150 + self._proj_warmup_steps = 1 + self._proj_speed_min = 30 + self._proj_speed_max = 40 + + num_actors = self.get_num_actors_per_env() + num_objs = len(PERTURB_OBJS) + self._proj_states = self._root_states.view( + self.num_envs, num_actors, self._root_states.shape[-1])[..., (num_actors - num_objs):, :] + + self._proj_actor_ids = num_actors * np.arange(self.num_envs) + self._proj_actor_ids = np.expand_dims(self._proj_actor_ids, axis=-1) + self._proj_actor_ids = self._proj_actor_ids + \ + np.reshape(np.array(self._proj_handles), [self.num_envs, num_objs]) + self._proj_actor_ids = self._proj_actor_ids.flatten() + self._proj_actor_ids = to_torch(self._proj_actor_ids, device=self.device, dtype=torch.int32) + + bodies_per_env = self._rigid_body_state.shape[0] // self.num_envs + contact_force_tensor = self.gym.acquire_net_contact_force_tensor(self.sim) + contact_force_tensor = gymtorch.wrap_tensor(contact_force_tensor) + self._proj_contact_forces = contact_force_tensor.view( + self.num_envs, bodies_per_env, 3)[..., (num_actors - num_objs):, :] + + self._calc_perturb_times() + + self.gym.subscribe_viewer_keyboard_event(self.viewer, gymapi.KEY_SPACE, "space_shoot") + self.gym.subscribe_viewer_keyboard_event(self.viewer, gymapi.KEY_R, "reset") + self.gym.subscribe_viewer_mouse_event(self.viewer, gymapi.MOUSE_LEFT_BUTTON, "mouse_shoot") + + def _load_proj_asset(self): + asset_root = "physhoi/data/assets/mjcf/" + + small_asset_file = "block_projectile.urdf" + # small_asset_file = "ball_medium.urdf" + small_asset_options = gymapi.AssetOptions() + small_asset_options.angular_damping = 0.01 + small_asset_options.linear_damping = 0.01 + small_asset_options.max_angular_velocity = 100.0 + small_asset_options.density = 200.0 + # small_asset_options.fix_base_link = True + small_asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE + self._small_proj_asset = self.gym.load_asset(self.sim, asset_root, small_asset_file, small_asset_options) + + def _load_marker_asset(self): + asset_root = "physhoi/data/assets/mjcf/" + asset_file = "location_marker.urdf" + + asset_options = gymapi.AssetOptions() + asset_options.angular_damping = 0.0 + asset_options.linear_damping = 0.0 + asset_options.max_angular_velocity = 0.0 + asset_options.density = 0 + asset_options.fix_base_link = True + asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE + + self._marker_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options) + + def _load_target_asset(self): # smplx + rofunc_path = get_rofunc_path() + asset_root = os.path.join(rofunc_path, "simulator/assets") + asset_file = "mjcf/basketball/ball.urdf" + + asset_options = gymapi.AssetOptions() + asset_options.angular_damping = 0.01 + asset_options.linear_damping = 0.01 + asset_options.max_angular_velocity = 100.0 + asset_options.density = 1000.0 + asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE + asset_options.vhacd_enabled = True + asset_options.vhacd_params.max_convex_hulls = 1 + asset_options.vhacd_params.max_num_vertices_per_ch = 64 + asset_options.vhacd_params.resolution = 300000 + # asset_options.vhacd_params.max_convex_hulls = 10 + # asset_options.disable_gravity = True + # asset_options.fix_base_link = True + + self._target_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options) + + def _build_target(self, env_id, env_ptr): + col_group = env_id + col_filter = 0 + segmentation_id = 0 + + default_pose = gymapi.Transform() + + target_handle = self.gym.create_actor(env_ptr, self._target_asset, default_pose, + "target", col_group, col_filter, segmentation_id) + + # set ball color + # if self.cfg["headless"] == False: + # self.gym.set_rigid_body_color(env_ptr, target_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(1.5, 1.5, 1.5)) + # # gymapi.Vec3(0., 1.0, 1.5)) + # + # rofunc_path = get_rofunc_path() + # asset_root = os.path.join(rofunc_path, "simulator/assets") + # texture_file = "mjcf/basketball/basketball.png" + # h = self.gym.create_texture_from_file(self.sim, os.path.join(asset_root, texture_file)) + # self.gym.set_rigid_body_texture(env_ptr, target_handle, 0, gymapi.MESH_VISUAL, h) + + self._target_handles.append(target_handle) + self.gym.set_actor_scale(env_ptr, target_handle, self.ball_size) + + def _build_marker(self, env_id, env_ptr): + col_group = env_id + col_filter = 2 + segmentation_id = 0 + + default_pose = gymapi.Transform() + + marker_handle = self.gym.create_actor(env_ptr, self._marker_asset, default_pose, + "marker", col_group, col_filter, segmentation_id) + self.gym.set_rigid_body_color(env_ptr, marker_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.8, 0.0, 0.0)) + self._marker_handles.append(marker_handle) + + def _build_target_tensors(self): + num_actors = self.get_num_actors_per_env() + self._target_states = self._root_states.view(self.num_envs, num_actors, self._root_states.shape[-1])[..., 1, :] + + self._tar_actor_ids = to_torch(num_actors * np.arange(self.num_envs), device=self.device, dtype=torch.int32) + 1 + + bodies_per_env = self._rigid_body_state.shape[0] // self.num_envs + contact_force_tensor = self.gym.acquire_net_contact_force_tensor(self.sim) + contact_force_tensor = gymtorch.wrap_tensor(contact_force_tensor) + self._tar_contact_forces = contact_force_tensor.view(self.num_envs, bodies_per_env, 3)[..., self.num_bodies, :] + + def _build_marker_state_tensors(self): + num_actors = self._root_states.shape[0] // self.num_envs + self._marker_states = self._root_states.view(self.num_envs, num_actors, self._root_states.shape[-1])[..., 2, :] + self._marker_pos = self._marker_states[..., :3] + self._marker_actor_ids = to_torch(num_actors * np.arange(self.num_envs), + device=self.device, dtype=torch.int32) + 2 + + def _reset_target(self, env_ids): + self._target_states[env_ids, :3] = self.hoi_data_dict[0]['obj_pos'][self.motion_times, :] # .clone()+0.5 + self._target_states[env_ids, 3:7] = self.hoi_data_dict[0]['obj_rot'][self.motion_times, :] # .clone() #rand_rot + self._target_states[env_ids, 7:10] = self.hoi_data_dict[0]['obj_pos_vel'][self.motion_times, :] # .clone() + self._target_states[env_ids, 10:13] = self.hoi_data_dict[0]['obj_rot_vel'][self.motion_times, :] # .clone() + + def _reset_env_tensors(self, env_ids): + super()._reset_env_tensors(env_ids) + + env_ids_int32 = self._tar_actor_ids[env_ids] + self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._root_states), + gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32)) + + def reset_idx(self, env_ids): + self._reset_default_env_ids = [] + self._reset_ref_env_ids = [] + + super().reset_idx(env_ids) + + def _reset_actors(self, env_ids): + if self._state_init == HumanoidPhysHOITask.StateInit.Default: + self._reset_default(env_ids) + elif (self._state_init == HumanoidPhysHOITask.StateInit.Start + or self._state_init == HumanoidPhysHOITask.StateInit.Random): + self._reset_ref_state_init(env_ids) + elif self._state_init == HumanoidPhysHOITask.StateInit.Hybrid: + self._reset_hybrid_state_init(env_ids) + else: + assert (False), "Unsupported state initialization strategy: {:s}".format(str(self._state_init)) + + self._reset_target(env_ids) + + def _reset_default(self, env_ids): + self._humanoid_root_states[env_ids] = self._initial_humanoid_root_states[env_ids] + self._dof_pos[env_ids] = self._initial_dof_pos[env_ids] + self._dof_vel[env_ids] = self._initial_dof_vel[env_ids] + self._reset_default_env_ids = env_ids + + def _reset_ref_state_init(self, env_ids): + num_envs = env_ids.shape[0] + + if (self._state_init == HumanoidPhysHOITask.StateInit.Random + or self._state_init == HumanoidPhysHOITask.StateInit.Hybrid): + motion_times = torch.randint( + 0, self.hoi_data_dict[0]['hoi_data'].shape[0] - 2, (num_envs,), device=self.device, dtype=torch.long) + elif (self._state_init == HumanoidPhysHOITask.StateInit.Start): + motion_times = torch.zeros(num_envs, device=self.device, dtype=torch.long) # .int() + + self.motion_times = motion_times.clone() + + # TODO: i should has shape of env_ids + i = random.randint(0, self.num_motions - 1) + + self._set_env_state(env_ids=env_ids, + root_pos=self.hoi_data_dict[i]['root_pos'][motion_times, :].clone(), + root_rot=self.hoi_data_dict[i]['root_rot'][motion_times, :].clone(), + dof_pos=self.hoi_data_dict[i]['dof_pos'][motion_times, :].clone(), + root_vel=self.hoi_data_dict[i]['root_pos_vel'][motion_times, :].clone(), + root_ang_vel=self.hoi_data_dict[i]['root_rot_vel'][motion_times, :].clone(), + dof_vel=self.hoi_data_dict[i]['dof_pos_vel'][motion_times, :].clone(), + ) + + def _reset_hybrid_state_init(self, env_ids): + num_envs = env_ids.shape[0] + ref_probs = to_torch(np.array([self._hybrid_init_prob] * num_envs), device=self.device) + ref_init_mask = torch.bernoulli(ref_probs) == 1.0 + + ref_reset_ids = env_ids[ref_init_mask] + if (len(ref_reset_ids) > 0): + self._reset_ref_state_init(ref_reset_ids) + + default_reset_ids = env_ids[torch.logical_not(ref_init_mask)] + if (len(default_reset_ids) > 0): + self._reset_default(default_reset_ids) + + def _set_env_state(self, env_ids, root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel): + self._humanoid_root_states[env_ids, 0:3] = root_pos + self._humanoid_root_states[env_ids, 3:7] = root_rot + self._humanoid_root_states[env_ids, 7:10] = root_vel + self._humanoid_root_states[env_ids, 10:13] = root_ang_vel + + self._dof_pos[env_ids] = dof_pos + self._dof_vel[env_ids] = dof_vel + + def _compute_hoi_observations(self, env_ids=None): + key_body_pos = self._rigid_body_pos[:, self._key_body_ids, :] + # diffvel, set 0 for the first frame + hist_dof_pos = self._hist_obs[:, 7:7 + 153] + dof_diffvel = (self._dof_pos - hist_dof_pos) * self.fps_data + dof_diffvel = dof_diffvel * (self.progress_buf != 1).to(float).unsqueeze(dim=-1) + + if (env_ids is None): + self._curr_obs[:] = build_hoi_observations(self._rigid_body_pos[:, 0, :], + self._rigid_body_rot[:, 0, :], + self._rigid_body_vel[:, 0, :], + self._rigid_body_ang_vel[:, 0, :], + self._dof_pos, self._dof_vel, key_body_pos, + self._local_root_obs, self._root_height_obs, + self._dof_obs_size, self._target_states, + dof_diffvel) + else: + self._curr_obs[env_ids] = build_hoi_observations(self._rigid_body_pos[env_ids][:, 0, :], + self._rigid_body_rot[env_ids][:, 0, :], + self._rigid_body_vel[env_ids][:, 0, :], + self._rigid_body_ang_vel[env_ids][:, 0, :], + self._dof_pos[env_ids], self._dof_vel[env_ids], + key_body_pos[env_ids], + self._local_root_obs, self._root_height_obs, + self._dof_obs_size, self._target_states[env_ids], + dof_diffvel[env_ids]) + + def _calc_perturb_times(self): + self._perturb_timesteps = [] + total_steps = 0 + for i, obj in enumerate(PERTURB_OBJS): + curr_time = obj[1] + total_steps += curr_time + self._perturb_timesteps.append(total_steps) + + self._perturb_timesteps = np.array(self._perturb_timesteps) + + def _update_proj(self): + + if self.projtype == 'Auto': + curr_timestep = self.progress_buf.cpu().numpy()[0] + curr_timestep = curr_timestep % (self._perturb_timesteps[-1] + 1) + perturb_step = np.where(self._perturb_timesteps == curr_timestep)[0] + + if (len(perturb_step) > 0): + perturb_id = perturb_step[0] + n = self.num_envs + humanoid_root_pos = self._humanoid_root_states[..., 0:3] + + rand_theta = torch.rand([n], dtype=self._proj_states.dtype, device=self._proj_states.device) + rand_theta *= 2 * np.pi + rand_dist = (self._proj_dist_max - self._proj_dist_min) * \ + torch.rand([n], dtype=self._proj_states.dtype, + device=self._proj_states.device) + self._proj_dist_min + pos_x = rand_dist * torch.cos(rand_theta) + pos_y = -rand_dist * torch.sin(rand_theta) + pos_z = (self._proj_h_max - self._proj_h_min) * \ + torch.rand([n], dtype=self._proj_states.dtype, + device=self._proj_states.device) + self._proj_h_min + + self._proj_states[..., perturb_id, 0] = humanoid_root_pos[..., 0] + pos_x + self._proj_states[..., perturb_id, 1] = humanoid_root_pos[..., 1] + pos_y + self._proj_states[..., perturb_id, 2] = pos_z + self._proj_states[..., perturb_id, 3:6] = 0.0 + self._proj_states[..., perturb_id, 6] = 1.0 + + tar_body_idx = np.random.randint(self.num_bodies) + tar_body_idx = 1 + + launch_tar_pos = self._rigid_body_pos[..., tar_body_idx, :] + launch_dir = launch_tar_pos - self._proj_states[..., perturb_id, 0:3] + launch_dir += 0.1 * torch.randn_like(launch_dir) + launch_dir = torch.nn.functional.normalize(launch_dir, dim=-1) + launch_speed = (self._proj_speed_max - self._proj_speed_min) * \ + torch.rand_like(launch_dir[:, 0:1]) + self._proj_speed_min + launch_vel = launch_speed * launch_dir + launch_vel[..., 0:2] += self._rigid_body_vel[..., tar_body_idx, 0:2] + self._proj_states[..., perturb_id, 7:10] = launch_vel + self._proj_states[..., perturb_id, 10:13] = 0.0 + + self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._root_states), + gymtorch.unwrap_tensor(self._proj_actor_ids), + len(self._proj_actor_ids)) + + elif self.projtype == 'Mouse': + # mouse control + for evt in self.gym.query_viewer_action_events(self.viewer): + + if evt.action == "reset" and evt.value > 0: + self.gym.set_sim_rigid_body_states(self.sim, self._proj_states, gymapi.STATE_ALL) + + elif (evt.action == "space_shoot" or evt.action == "mouse_shoot") and evt.value > 0: + if evt.action == "mouse_shoot": + pos = self.gym.get_viewer_mouse_position(self.viewer) + window_size = self.gym.get_viewer_size(self.viewer) + xcoord = round(pos.x * window_size.x) + ycoord = round(pos.y * window_size.y) + print(f"Fired projectile with mouse at coords: {xcoord} {ycoord}") + + cam_pose = self.gym.get_viewer_camera_transform(self.viewer, None) + cam_fwd = cam_pose.r.rotate(gymapi.Vec3(0, 0, 1)) + + spawn = cam_pose.p + speed = 25 + vel = cam_fwd * speed + + angvel = 1.57 - 3.14 * np.random.random(3) + + self._proj_states[..., 0] = spawn.x + self._proj_states[..., 1] = spawn.y + self._proj_states[..., 2] = spawn.z + self._proj_states[..., 7] = vel.x + self._proj_states[..., 8] = vel.y + self._proj_states[..., 9] = vel.z + + self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._root_states), + gymtorch.unwrap_tensor(self._proj_actor_ids), + len(self._proj_actor_ids)) + + def play_dataset_step(self, time): + + t = time + + ### update object ### + self._target_states[:, :3] = self.hoi_data_dict[0]['obj_pos'][t, :] + self._target_states[:, 3:7] = self.hoi_data_dict[0]['obj_rot'][t, :] + self._target_states[:, 7:10] = torch.zeros_like(self._target_states[:, 7:10]) + self._target_states[:, 10:13] = torch.zeros_like(self._target_states[:, 10:13]) + + ### update subject ### + _humanoid_root_pos = self.hoi_data_dict[0]['root_pos'][t, :].clone() + _humanoid_root_rot = self.hoi_data_dict[0]['root_rot'][t, :].clone() + self._humanoid_root_states[:, 0:3] = _humanoid_root_pos + self._humanoid_root_states[:, 3:7] = _humanoid_root_rot + self._humanoid_root_states[:, 7:10] = torch.zeros_like(self._humanoid_root_states[:, 7:10]) + self._humanoid_root_states[:, 10:13] = torch.zeros_like(self._humanoid_root_states[:, 10:13]) + + self._dof_pos[:] = self.hoi_data_dict[0]['dof_pos'][t, :].clone() + self._dof_vel[:] = torch.zeros_like(self._dof_vel[:]) + + contact = self.hoi_data_dict[0]['contact'][t, :] + + self.gym.set_actor_root_state_tensor(self.sim, gymtorch.unwrap_tensor(self._root_states)) + self.gym.set_dof_state_tensor(self.sim, gymtorch.unwrap_tensor(self._dof_state)) + self._refresh_sim_tensors() + + # ### draw contact label ### + obj_contact = torch.any(contact > 0.1, dim=-1) + for env_id, env_ptr in enumerate(self.envs): + env_ptr = self.envs[env_id] + handle = self._target_handles[env_id] + + if obj_contact == True: + self.gym.set_rigid_body_color(env_ptr, handle, 0, gymapi.MESH_VISUAL, + gymapi.Vec3(1., 0., 0.)) + else: + self.gym.set_rigid_body_color(env_ptr, handle, 0, gymapi.MESH_VISUAL, + gymapi.Vec3(0., 1., 0.)) + + self.render(t=t) + self.gym.simulate(self.sim) + + def get_dataset_step(self, time): + t = time + + ### update object ### + self._target_states[:, :3] = self.hoi_data_dict[0]['obj_pos'][t, :] + self._target_states[:, 3:7] = self.hoi_data_dict[0]['obj_rot'][t, :] + self._target_states[:, 7:10] = torch.zeros_like(self._target_states[:, 7:10]) + self._target_states[:, 10:13] = torch.zeros_like(self._target_states[:, 10:13]) + + ### update subject ### + _humanoid_root_pos = self.hoi_data_dict[0]['root_pos'][t, :].clone() + _humanoid_root_rot = self.hoi_data_dict[0]['root_rot'][t, :].clone() + self._humanoid_root_states[:, 0:3] = _humanoid_root_pos + self._humanoid_root_states[:, 3:7] = _humanoid_root_rot + self._humanoid_root_states[:, 7:10] = torch.zeros_like(self._humanoid_root_states[:, 7:10]) + self._humanoid_root_states[:, 10:13] = torch.zeros_like(self._humanoid_root_states[:, 10:13]) + + self._dof_pos[:] = self.hoi_data_dict[0]['dof_pos'][t, :].clone() + self._dof_vel[:] = torch.zeros_like(self._dof_vel[:]) + + contact = self.hoi_data_dict[0]['contact'][t, :] + + return self._dof_state + + def _draw_task_play(self, t): + # self._update_marker() + + cols = np.array([[1.0, 0.0, 0.0]], dtype=np.float32) # color + + self.gym.clear_lines(self.viewer) + + starts = self.hoi_data_dict[0]['hoi_data'][t, :3] + + for i, env_ptr in enumerate(self.envs): + for j in range(len(self._key_body_ids)): + vec = self.hoi_data_dict[0]['key_body_pos'][t, j * 3:j * 3 + 3] + vec = torch.cat([starts, vec], dim=-1).cpu().numpy().reshape([1, 6]) + self.gym.add_lines(self.viewer, env_ptr, 1, vec, cols) + + def render(self, sync_frame_time=False, t=0): + super().render(sync_frame_time) + + if self.viewer: + # self._draw_task() + + if self.save_images: + env_ids = 0 + if self.play_dataset: + frame_id = t + else: + frame_id = self.progress_buf[env_ids] + dataname = self.motion_file[len('physhoi/data/motions/BallPlay/'):-3] + rgb_filename = "physhoi/data/images/" + dataname + "/rgb_env%d_frame%05d.png" % (env_ids, frame_id) + os.makedirs("physhoi/data/images/" + dataname, exist_ok=True) + self.gym.write_viewer_image_to_file(self.viewer, rgb_filename) + + # def _draw_task(self): + # # self._update_marker() + # + # # # draw obj contact + # # obj_contact = torch.any(torch.abs(self._tar_contact_forces[..., 0:2]) > 0.1, dim=-1) + # # for env_id, env_ptr in enumerate(self.envs): + # # env_ptr = self.envs[env_id] + # # handle = self._target_handles[env_id] + # + # # if obj_contact[env_id] == True: + # # self.gym.set_rigid_body_color(env_ptr, handle, 0, gymapi.MESH_VISUAL, + # # gymapi.Vec3(1., 0., 0.)) + # # else: + # # self.gym.set_rigid_body_color(env_ptr, handle, 0, gymapi.MESH_VISUAL, + # # gymapi.Vec3(0., 1., 0.)) + + +##################################################################### +### =========================jit functions=========================### +##################################################################### + +#@torch.jit.script +def build_hoi_observations(root_pos, root_rot, root_vel, root_ang_vel, dof_pos, dof_vel, key_body_pos, + local_root_obs, root_height_obs, dof_obs_size, target_states, dof_diffvel): + contact = torch.zeros(key_body_pos.shape[0], 1).cuda() + obs = torch.cat((root_pos, root_rot, dof_pos, dof_diffvel, target_states[:, :10], key_body_pos.contiguous( + ).view(-1, key_body_pos.shape[1] * key_body_pos.shape[2]), contact), dim=-1) + return obs + + +@torch.jit.script +def compute_obj_observations(root_states, tar_states): + # type: (Tensor, Tensor) -> Tensor + root_pos = root_states[:, 0:3] + root_rot = root_states[:, 3:7] + + tar_pos = tar_states[:, 0:3] + tar_rot = tar_states[:, 3:7] + tar_vel = tar_states[:, 7:10] + tar_ang_vel = tar_states[:, 10:13] + + heading_rot = torch_utils.calc_heading_quat_inv(root_rot) + + local_tar_pos = tar_pos - root_pos + local_tar_pos[..., -1] = tar_pos[..., -1] + local_tar_pos = quat_rotate(heading_rot, local_tar_pos) + local_tar_vel = quat_rotate(heading_rot, tar_vel) + local_tar_ang_vel = quat_rotate(heading_rot, tar_ang_vel) + + local_tar_rot = quat_mul(heading_rot, tar_rot) + local_tar_rot_obs = torch_utils.quat_to_tan_norm(local_tar_rot) + + obs = torch.cat([local_tar_pos, local_tar_rot_obs, local_tar_vel, local_tar_ang_vel], dim=-1) + return obs + + +@torch.jit.script +def compute_humanoid_observations_max(body_pos, body_rot, body_vel, body_ang_vel, local_root_obs, root_height_obs, + contact_forces, contact_body_ids): + # type: (Tensor, Tensor, Tensor, Tensor, bool, bool, Tensor, Tensor) -> Tensor + root_pos = body_pos[:, 0, :] + root_rot = body_rot[:, 0, :] + + root_h = root_pos[:, 2:3] + heading_rot = torch_utils.calc_heading_quat_inv(root_rot) + + if (not root_height_obs): + root_h_obs = torch.zeros_like(root_h) + else: + root_h_obs = root_h + + heading_rot_expand = heading_rot.unsqueeze(-2) + heading_rot_expand = heading_rot_expand.repeat((1, body_pos.shape[1], 1)) + flat_heading_rot = heading_rot_expand.reshape(heading_rot_expand.shape[0] * heading_rot_expand.shape[1], + heading_rot_expand.shape[2]) + + root_pos_expand = root_pos.unsqueeze(-2) + local_body_pos = body_pos - root_pos_expand + flat_local_body_pos = local_body_pos.reshape( + local_body_pos.shape[0] * local_body_pos.shape[1], local_body_pos.shape[2]) + flat_local_body_pos = quat_rotate(flat_heading_rot, flat_local_body_pos) + local_body_pos = flat_local_body_pos.reshape( + local_body_pos.shape[0], local_body_pos.shape[1] * local_body_pos.shape[2]) + local_body_pos = local_body_pos[..., 3:] # remove root pos + + flat_body_rot = body_rot.reshape(body_rot.shape[0] * body_rot.shape[1], body_rot.shape[2]) + flat_local_body_rot = quat_mul(flat_heading_rot, flat_body_rot) + flat_local_body_rot_obs = torch_utils.quat_to_tan_norm(flat_local_body_rot) + local_body_rot_obs = flat_local_body_rot_obs.reshape( + body_rot.shape[0], body_rot.shape[1] * flat_local_body_rot_obs.shape[1]) + + if (local_root_obs): + root_rot_obs = torch_utils.quat_to_tan_norm(root_rot) + local_body_rot_obs[..., 0:6] = root_rot_obs + + flat_body_vel = body_vel.reshape(body_vel.shape[0] * body_vel.shape[1], body_vel.shape[2]) + flat_local_body_vel = quat_rotate(flat_heading_rot, flat_body_vel) + local_body_vel = flat_local_body_vel.reshape(body_vel.shape[0], body_vel.shape[1] * body_vel.shape[2]) + + flat_body_ang_vel = body_ang_vel.reshape(body_ang_vel.shape[0] * body_ang_vel.shape[1], body_ang_vel.shape[2]) + flat_local_body_ang_vel = quat_rotate(flat_heading_rot, flat_body_ang_vel) + local_body_ang_vel = flat_local_body_ang_vel.reshape( + body_ang_vel.shape[0], body_ang_vel.shape[1] * body_ang_vel.shape[2]) + + body_contact_buf = contact_forces[:, contact_body_ids, :].clone().view(contact_forces.shape[0], -1) + + obs = torch.cat((root_h_obs, local_body_pos, local_body_rot_obs, + local_body_vel, local_body_ang_vel, body_contact_buf), dim=-1) + return obs + + +@torch.jit.script +def compute_humanoid_reward(hoi_ref, hoi_obs, contact_buf, tar_contact_forces, len_keypos, w_p, w_r, w_pv, w_rv, w_op, + w_or, w_opv, w_orv, w_ig, w_cg1, w_cg2): + # type: (Tensor, Tensor, Tensor, Tensor, int, float, float, float, float, float, float, float, float, float, float, float) -> Tensor + + ### data preprocess ### + + # simulated states + root_pos = hoi_obs[:, :3] + root_rot = hoi_obs[:, 3:3 + 4] + dof_pos = hoi_obs[:, 7:7 + 51 * 3] + dof_pos_vel = hoi_obs[:, 160:160 + 51 * 3] + obj_pos = hoi_obs[:, 313:313 + 3] + obj_rot = hoi_obs[:, 316:316 + 4] + obj_pos_vel = hoi_obs[:, 320:320 + 3] + key_pos = hoi_obs[:, 323:323 + len_keypos * 3] + contact = hoi_obs[:, -1:] # fake one + key_pos = torch.cat((root_pos, key_pos), dim=-1) + body_rot = torch.cat((root_rot, dof_pos), dim=-1) + ig = key_pos.view(-1, len_keypos + 1, 3).transpose(0, 1) - obj_pos[:, :3] + ig = ig.transpose(0, 1).view(-1, (len_keypos + 1) * 3) + + # reference states + ref_root_pos = hoi_ref[:, :3] + ref_root_rot = hoi_ref[:, 3:3 + 4] + ref_dof_pos = hoi_ref[:, 7:7 + 51 * 3] + ref_dof_pos_vel = hoi_ref[:, 160:160 + 51 * 3] + ref_obj_pos = hoi_ref[:, 313:313 + 3] + ref_obj_rot = hoi_ref[:, 316:316 + 4] + ref_obj_pos_vel = hoi_ref[:, 320:320 + 3] + ref_key_pos = hoi_ref[:, 323:323 + len_keypos * 3] + ref_obj_contact = hoi_ref[:, -1:] + ref_key_pos = torch.cat((ref_root_pos, ref_key_pos), dim=-1) + ref_body_rot = torch.cat((ref_root_rot, ref_dof_pos), dim=-1) + ref_ig = ref_key_pos.view(-1, len_keypos + 1, 3).transpose(0, 1) - ref_obj_pos[:, :3] + ref_ig = ref_ig.transpose(0, 1).view(-1, (len_keypos + 1) * 3) + + ### body reward ### + + # body pos reward + ep = torch.mean((ref_key_pos - key_pos) ** 2, dim=-1) + rp = torch.exp(-ep * w_p) + + # body rot reward + er = torch.mean((ref_body_rot - body_rot) ** 2, dim=-1) + rr = torch.exp(-er * w_r) + + # body pos vel reward + epv = torch.zeros_like(ep) + rpv = torch.exp(-epv * w_pv) + + # body rot vel reward + erv = torch.mean((ref_dof_pos_vel - dof_pos_vel) ** 2, dim=-1) + rrv = torch.exp(-erv * w_rv) + + rb = rp * rr * rpv * rrv + + ### object reward ### + + # object pos reward + eop = torch.mean((ref_obj_pos - obj_pos) ** 2, dim=-1) + rop = torch.exp(-eop * w_op) + + # object rot reward + eor = torch.zeros_like(ep) # torch.mean((ref_obj_rot - obj_rot)**2,dim=-1) + ror = torch.exp(-eor * w_or) + + # object pos vel reward + eopv = torch.mean((ref_obj_pos_vel - obj_pos_vel) ** 2, dim=-1) + ropv = torch.exp(-eopv * w_opv) + + # object rot vel reward + eorv = torch.zeros_like(ep) # torch.mean((ref_obj_rot_vel - obj_rot_vel)**2,dim=-1) + rorv = torch.exp(-eorv * w_orv) + + ro = rop * ror * ropv * rorv + + ### interaction graph reward ### + + eig = torch.mean((ref_ig - ig) ** 2, dim=-1) + rig = torch.exp(-eig * w_ig) + + ### simplified contact graph reward ### + + # Since Isaac Gym does not yet provide API for detailed collision detection in GPU pipeline, + # we use force detection to approximate the contact status. + # In this case we use the CG node istead of the CG edge for imitation. + # TODO: update the code once collision detection API is available. + + # body ids + # Pelvis, 0 + # L_Hip, 1 + # L_Knee, 2 + # L_Ankle, 3 + # L_Toe, 4 + # R_Hip, 5 + # R_Knee, 6 + # R_Ankle, 7 + # R_Toe, 8 + # Torso, 9 + # Spine, 10 + # Chest, 11 + # Neck, 12 + # Head, 13 + # L_Thorax, 14 + # L_Shoulder, 15 + # L_Elbow, 16 + # L_Wrist, 17 + # L_Hand, 18-32 + # R_Thorax, 33 + # R_Shoulder, 34 + # R_Elbow, 35 + # R_Wrist, 36 + # R_Hand, 37-51 + + # body contact + contact_body_ids = [0, 1, 2, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 33, 34, 35] + body_contact_buf = contact_buf[:, contact_body_ids, :].clone() + body_contact = torch.all(torch.abs(body_contact_buf) < 0.1, dim=-1) + body_contact = torch.all(body_contact, dim=-1) # =1 when no contact happens to the body + + # object contact + # =1 when contact happens to the object + obj_contact = torch.any(torch.abs(tar_contact_forces[..., 0:2]) > 0.1, dim=-1) + + ref_body_contact = torch.ones_like(ref_obj_contact) # no body contact for all time + ecg1 = torch.abs(body_contact - ref_body_contact[:, 0]) + rcg1 = torch.exp(-ecg1 * w_cg1) + ecg2 = torch.abs(obj_contact - ref_obj_contact[:, 0]) + rcg2 = torch.exp(-ecg2 * w_cg2) + + rcg = rcg1 * rcg2 + + ### task-agnostic HOI imitation reward ### + reward = rb * ro * rig * rcg + + return reward + + +@torch.jit.script +def compute_humanoid_reset(reset_buf, progress_buf, contact_buf, rigid_body_pos, + max_episode_length, enable_early_termination, termination_heights, hoi_ref, hoi_obs): + # type: (Tensor, Tensor, Tensor, Tensor, float, bool, Tensor, Tensor, Tensor) -> Tuple[Tensor, Tensor] + terminated = torch.zeros_like(reset_buf) + + if (enable_early_termination): + body_height = rigid_body_pos[:, 0, 2] # root height + body_fall = body_height < termination_heights # [4096] + has_failed = body_fall.clone() + has_failed *= (progress_buf > 1) + + terminated = torch.where(has_failed, torch.ones_like(reset_buf), terminated) + + reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), terminated) + + return reset, terminated diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/motion_lib.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/motion_lib.py new file mode 100644 index 000000000..e61a14f3a --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/motion_lib.py @@ -0,0 +1,391 @@ +# Copyright (c) 2018-2022, NVIDIA Corporation +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import os +import random + +import yaml +from isaacgym.torch_utils import * + +from rofunc.learning.RofuncRL.tasks.utils import torch_jit_utils as torch_utils +from rofunc.utils.datalab.poselib.poselib.core.rotation3d import * +from rofunc.utils.datalab.poselib.poselib.skeleton.skeleton3d import SkeletonMotion + +USE_CACHE = True +print("MOVING MOTION DATA TO GPU, USING CACHE:", USE_CACHE) + +if not USE_CACHE: + old_numpy = torch.Tensor.numpy + + + class Patch: + def numpy(self): + if self.is_cuda: + return self.to("cpu").numpy() + else: + return old_numpy(self) + + + torch.Tensor.numpy = Patch.numpy + + +class DeviceCache: + def __init__(self, obj, device): + self.obj = obj + self.device = device + + keys = dir(obj) + num_added = 0 + for k in keys: + try: + out = getattr(obj, k) + except: + print("Error for key=", k) + continue + + if isinstance(out, torch.Tensor): + if out.is_floating_point(): + out = out.to(self.device, dtype=torch.float32) + else: + out.to(self.device) + setattr(self, k, out) + num_added += 1 + elif isinstance(out, np.ndarray): + out = torch.tensor(out) + if out.is_floating_point(): + out = out.to(self.device, dtype=torch.float32) + else: + out.to(self.device) + setattr(self, k, out) + num_added += 1 + + print("Total added", num_added) + + def __getattr__(self, string): + out = getattr(self.obj, string) + return out + + +class MotionLib: + def __init__(self, motion_file, dof_body_ids, dof_offsets, key_body_ids, device): + """ + + Args: + motion_file: + dof_body_ids: + dof_offsets: + key_body_ids: + device: + """ + self._dof_body_ids = dof_body_ids + self._dof_offsets = dof_offsets + self._num_dof = dof_offsets[-1] + self._key_body_ids = torch.tensor(key_body_ids, device=device) + self._device = device + self._load_motions(motion_file) + + motions = self._motions + self.gts = torch.cat([m.global_translation for m in motions], dim=0).float() + self.grs = torch.cat([m.global_rotation for m in motions], dim=0).float() + self.lrs = torch.cat([m.local_rotation for m in motions], dim=0).float() + self.grvs = torch.cat([m.global_root_velocity for m in motions], dim=0).float() + self.gravs = torch.cat([m.global_root_angular_velocity for m in motions], dim=0).float() + self.dvs = torch.cat([m.dof_vels for m in motions], dim=0).float() + + lengths = self._motion_num_frames + lengths_shifted = lengths.roll(1) + lengths_shifted[0] = 0 + self.length_starts = lengths_shifted.cumsum(0) + + self.motion_ids = torch.arange(len(self._motions), dtype=torch.long, device=self._device) + + def num_motions(self): + return len(self._motions) + + def get_total_length(self): + return sum(self._motion_lengths) + + def get_motion(self, motion_id): + return self._motions[motion_id] + + def sample_motions(self, n): + motion_ids = torch.multinomial(self._motion_weights, num_samples=n, replacement=True) + + # m = self.num_motions() + # motion_ids = np.random.choice(m, size=n, replace=True, p=self._motion_weights) + # motion_ids = torch.tensor(motion_ids, device=self._device, dtype=torch.long) + return motion_ids + + def sample_time(self, motion_ids, truncate_time=None): + n = len(motion_ids) + # phase = torch.rand(motion_ids.shape, device=self._device) + phase = torch.Tensor(random.choices(range(0, 25), k=motion_ids.shape[0])).to("cuda") + phase = phase / 25. + + motion_len = self._motion_lengths[motion_ids] + if truncate_time is not None: + assert truncate_time >= 0.0 + motion_len -= truncate_time + + motion_time = phase * motion_len + return motion_time + + def get_motion_length(self, motion_ids): + return self._motion_lengths[motion_ids] + + def get_motion_state(self, motion_ids, motion_times): + n = len(motion_ids) + num_bodies = self._get_num_bodies() + num_key_bodies = self._key_body_ids.shape[0] + + motion_len = self._motion_lengths[motion_ids] + num_frames = self._motion_num_frames[motion_ids] + dt = self._motion_dt[motion_ids] + + frame_idx0, frame_idx1, blend = self._calc_frame_blend(motion_times, motion_len, num_frames, dt) + + f0l = frame_idx0 + self.length_starts[motion_ids] + f1l = frame_idx1 + self.length_starts[motion_ids] + + root_pos0 = self.gts[f0l, 0] + root_pos1 = self.gts[f1l, 0] + + root_rot0 = self.grs[f0l, 0] + root_rot1 = self.grs[f1l, 0] + + local_rot0 = self.lrs[f0l] + local_rot1 = self.lrs[f1l] + + root_vel = self.grvs[f0l] + + root_ang_vel = self.gravs[f0l] + + key_pos0 = self.gts[f0l.unsqueeze(-1), self._key_body_ids.unsqueeze(0)] + key_pos1 = self.gts[f1l.unsqueeze(-1), self._key_body_ids.unsqueeze(0)] + + dof_vel = self.dvs[f0l] + + vals = [root_pos0, root_pos1, local_rot0, local_rot1, root_vel, root_ang_vel, key_pos0, key_pos1] + for v in vals: + assert v.dtype != torch.float64 + + blend = blend.unsqueeze(-1) + + root_pos = (1.0 - blend) * root_pos0 + blend * root_pos1 + + root_rot = torch_utils.slerp(root_rot0, root_rot1, blend) + + blend_exp = blend.unsqueeze(-1) + key_pos = (1.0 - blend_exp) * key_pos0 + blend_exp * key_pos1 + + local_rot = torch_utils.slerp(local_rot0, local_rot1, torch.unsqueeze(blend, axis=-1)) + dof_pos = self._local_rotation_to_dof(local_rot) + + return root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel, key_pos + + def _load_motions(self, motion_file): + self._motions = [] + self._motion_lengths = [] + self._motion_weights = [] + self._motion_fps = [] + self._motion_dt = [] + self._motion_num_frames = [] + self._motion_files = [] + + total_len = 0.0 + + motion_files, motion_weights = self._fetch_motion_files(motion_file) + num_motion_files = len(motion_files) + import tqdm + + with tqdm.trange(num_motion_files, ncols=100, colour="green") as t_bar: + for f in t_bar: + curr_file = motion_files[f] + t_bar.set_postfix_str("Loading: {:s}".format(curr_file.split("/")[-1])) + curr_motion = SkeletonMotion.from_file(curr_file) + + motion_fps = curr_motion.fps + curr_dt = 1.0 / motion_fps + + num_frames = curr_motion.tensor.shape[0] + curr_len = 1.0 / motion_fps * (num_frames - 1) + + self._motion_fps.append(motion_fps) + self._motion_dt.append(curr_dt) + self._motion_num_frames.append(num_frames) + + curr_dof_vels = self._compute_motion_dof_vels(curr_motion) + curr_motion.dof_vels = curr_dof_vels + + # Moving motion tensors to the GPU + if USE_CACHE: + curr_motion = DeviceCache(curr_motion, self._device) + else: + curr_motion.tensor = curr_motion.tensor.to(self._device) + curr_motion._skeleton_tree._parent_indices = curr_motion._skeleton_tree._parent_indices.to( + self._device) + curr_motion._skeleton_tree._local_translation = curr_motion._skeleton_tree._local_translation.to( + self._device) + curr_motion._rotation = curr_motion._rotation.to(self._device) + + self._motions.append(curr_motion) + self._motion_lengths.append(curr_len) + + curr_weight = motion_weights[f] + self._motion_weights.append(curr_weight) + self._motion_files.append(curr_file) + + self._motion_lengths = torch.tensor(self._motion_lengths, device=self._device, dtype=torch.float32) + + self._motion_weights = torch.tensor(self._motion_weights, dtype=torch.float32, device=self._device) + self._motion_weights /= self._motion_weights.sum() + + self._motion_fps = torch.tensor(self._motion_fps, device=self._device, dtype=torch.float32) + self._motion_dt = torch.tensor(self._motion_dt, device=self._device, dtype=torch.float32) + self._motion_num_frames = torch.tensor(self._motion_num_frames, device=self._device) + + num_motions = self.num_motions() + total_len = self.get_total_length() + + print("Loaded {:d} motions with a total length of {:.3f}s.".format(num_motions, total_len)) + + def _fetch_motion_files(self, motion_file): + ext = os.path.splitext(motion_file)[1] + if ext == ".yaml": + dir_name = os.path.dirname(motion_file) + motion_files = [] + motion_weights = [] + + with open(os.path.join(os.getcwd(), motion_file), "r") as f: + motion_config = yaml.load(f, Loader=yaml.SafeLoader) + + motion_list = motion_config["motions"] + for motion_entry in motion_list: + curr_file = motion_entry["file"] + curr_weight = motion_entry["weight"] + assert curr_weight >= 0 + + curr_file = os.path.join(dir_name, curr_file) + motion_weights.append(curr_weight) + motion_files.append(curr_file) + else: + motion_files = [motion_file] + motion_weights = [1.0] + + return motion_files, motion_weights + + def _calc_frame_blend(self, time, len, num_frames, dt): + + phase = time / len + phase = torch.clip(phase, 0.0, 1.0) + + frame_idx0 = (phase * (num_frames - 1)).long() + frame_idx1 = torch.min(frame_idx0 + 1, num_frames - 1) + blend = (time - frame_idx0 * dt) / dt + + return frame_idx0, frame_idx1, blend + + def _get_num_bodies(self): + motion = self.get_motion(0) + num_bodies = motion.num_joints + return num_bodies + + def _compute_motion_dof_vels(self, motion): + num_frames = motion.tensor.shape[0] + dt = 1.0 / motion.fps + dof_vels = [] + + for f in range(num_frames - 1): + local_rot0 = motion.local_rotation[f] + local_rot1 = motion.local_rotation[f + 1] + frame_dof_vel = self._local_rotation_to_dof_vel(local_rot0, local_rot1, dt) + frame_dof_vel = frame_dof_vel + dof_vels.append(frame_dof_vel) + + dof_vels.append(dof_vels[-1]) + dof_vels = torch.stack(dof_vels, dim=0) + + return dof_vels + + def _local_rotation_to_dof(self, local_rot): + body_ids = self._dof_body_ids + dof_offsets = self._dof_offsets + + n = local_rot.shape[0] + dof_pos = torch.zeros((n, self._num_dof), dtype=torch.float, device=self._device) + + for j in range(len(body_ids)): + body_id = body_ids[j] + joint_offset = dof_offsets[j] + joint_size = dof_offsets[j + 1] - joint_offset + + if joint_size == 3: + joint_q = local_rot[:, body_id] + joint_exp_map = torch_utils.quat_to_exp_map(joint_q) + dof_pos[:, joint_offset:(joint_offset + joint_size)] = joint_exp_map + elif joint_size == 1: + joint_q = local_rot[:, body_id] + joint_theta, joint_axis = torch_utils.quat_to_angle_axis(joint_q) + joint_theta = joint_theta * joint_axis[..., 1] # assume joint is always along y axis + + joint_theta = normalize_angle(joint_theta) + dof_pos[:, joint_offset] = joint_theta + + else: + print("Unsupported joint type") + assert False + + return dof_pos + + def _local_rotation_to_dof_vel(self, local_rot0, local_rot1, dt): + body_ids = self._dof_body_ids + dof_offsets = self._dof_offsets + + dof_vel = torch.zeros([self._num_dof], device=self._device) + + diff_quat_data = quat_mul_norm(quat_inverse(local_rot0), local_rot1) + diff_angle, diff_axis = quat_angle_axis(diff_quat_data) + local_vel = diff_axis * diff_angle.unsqueeze(-1) / dt + + for j in range(len(body_ids)): + body_id = body_ids[j] + joint_offset = dof_offsets[j] + joint_size = dof_offsets[j + 1] - joint_offset + if joint_size == 3: + joint_vel = local_vel[body_id] + dof_vel[joint_offset:(joint_offset + joint_size)] = joint_vel + elif joint_size == 1: + assert joint_size == 1 + joint_vel = local_vel[body_id] + dof_vel[joint_offset] = joint_vel[1] # assume joint is always along y axis + + else: + print("Unsupported joint type") + assert False + + return dof_vel diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/physhoi.py similarity index 70% rename from rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi.py rename to rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/physhoi.py index 9bdd9dd85..ffcb559b4 100644 --- a/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi.py +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/physhoi/physhoi.py @@ -35,10 +35,11 @@ from isaacgym import gymapi from isaacgym.torch_utils import * -from utils import torch_utils - -from .tasks.base_task import BaseTask +from rofunc.learning.RofuncRL.tasks.utils import torch_jit_utils as torch_utils +from .base_task import BaseTask +from rofunc.utils.oslab import get_rofunc_path +import rofunc as rf PERTURB_OBJS = [ ["small", 60], @@ -60,11 +61,11 @@ def __init__(self, cfg, sim_params, physics_engine, device_type, device_id, head self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"] self.plane_restitution = self.cfg["env"]["plane"]["restitution"] - self.ref_hoi_obs_size = 324 + len(self.cfg["env"]["keyBodies"])*3 + self.ref_hoi_obs_size = 324 + len(self.cfg["env"]["keyBodies"]) * 3 self._local_root_obs = self.cfg["env"]["localRootObs"] self._root_height_obs = self.cfg["env"].get("rootHeightObs", True) self._enable_early_termination = self.cfg["env"]["enableEarlyTermination"] - + key_bodies = self.cfg["env"]["keyBodies"] self._setup_character_props(key_bodies) @@ -74,11 +75,11 @@ def __init__(self, cfg, sim_params, physics_engine, device_type, device_id, head self.cfg["device_type"] = device_type self.cfg["device_id"] = device_id self.cfg["headless"] = headless - + super().__init__(cfg=self.cfg) - + self.dt = self.control_freq_inv * sim_params.dt - + # get gym GPU state tensors actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim) dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim) @@ -91,7 +92,7 @@ def __init__(self, cfg, sim_params, physics_engine, device_type, device_id, head dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim) self.dof_force_tensor = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs, self.num_dof) - + self.gym.refresh_dof_state_tensor(self.sim) self.gym.refresh_actor_root_state_tensor(self.sim) self.gym.refresh_rigid_body_state_tensor(self.sim) @@ -99,8 +100,9 @@ def __init__(self, cfg, sim_params, physics_engine, device_type, device_id, head self._root_states = gymtorch.wrap_tensor(actor_root_state) num_actors = self.get_num_actors_per_env() - - self._humanoid_root_states = self._root_states.view(self.num_envs, num_actors, actor_root_state.shape[-1])[..., 0, :] + + self._humanoid_root_states = self._root_states.view(self.num_envs, num_actors, actor_root_state.shape[-1])[..., + 0, :] self._initial_humanoid_root_states = self._humanoid_root_states.clone() self._initial_humanoid_root_states[:, 7:13] = 0 @@ -111,10 +113,10 @@ def __init__(self, cfg, sim_params, physics_engine, device_type, device_id, head dofs_per_env = self._dof_state.shape[0] // self.num_envs self._dof_pos = self._dof_state.view(self.num_envs, dofs_per_env, 2)[..., :self.num_dof, 0] self._dof_vel = self._dof_state.view(self.num_envs, dofs_per_env, 2)[..., :self.num_dof, 1] - + self._initial_dof_pos = torch.zeros_like(self._dof_pos, device=self.device, dtype=torch.float) self._initial_dof_vel = torch.zeros_like(self._dof_vel, device=self.device, dtype=torch.float) - + self._rigid_body_state = gymtorch.wrap_tensor(rigid_body_state) bodies_per_env = self._rigid_body_state.shape[0] // self.num_envs rigid_body_state_reshaped = self._rigid_body_state.view(self.num_envs, bodies_per_env, 13) @@ -126,18 +128,18 @@ def __init__(self, cfg, sim_params, physics_engine, device_type, device_id, head contact_force_tensor = gymtorch.wrap_tensor(contact_force_tensor) self._contact_forces = contact_force_tensor.view(self.num_envs, bodies_per_env, 3)[..., :self.num_bodies, :] - + self._terminate_buf = torch.ones(self.num_envs, device=self.device, dtype=torch.long) - + self._build_termination_heights() - + contact_bodies = self.cfg["env"]["contactBodies"] self._key_body_ids = self._build_key_body_ids_tensor(key_bodies) self._contact_body_ids = self._build_contact_body_ids_tensor(contact_bodies) - + if self.viewer != None: self._init_camera() - + return def get_obs_size(self): @@ -158,11 +160,19 @@ def create_sim(self): self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs))) return - def reset(self, env_ids=None): - if (env_ids is None): - env_ids = to_torch(np.arange(self.num_envs), device=self.device, dtype=torch.long) - self._reset_envs(env_ids) - return + # def reset(self, env_ids=None): + # if (env_ids is None): + # env_ids = to_torch(np.arange(self.num_envs), device=self.device, dtype=torch.long) + # self.reset_idx(env_ids) + + def reset(self): + actions = 0.01 * (1 - 2 * np.random.rand(self.num_envs, self.num_actions)).astype('f') + actions = torch.tensor(actions, dtype=torch.float) + + # step the simulator + obs, rewards, resets, extras = self.step(actions) + + return to_torch(obs["obs"], dtype=torch.float, device=self.rl_device) def set_char_color(self, col, env_ids): for env_id in env_ids: @@ -175,13 +185,20 @@ def set_char_color(self, col, env_ids): return - def _reset_envs(self, env_ids): - if (len(env_ids) > 0): + # def _reset_envs(self, env_ids): + # if (len(env_ids) > 0): + # self._reset_actors(env_ids) + # self._reset_env_tensors(env_ids) + # self._refresh_sim_tensors() + # self._compute_observations(env_ids) + # return + + def reset_idx(self, env_ids): + if len(env_ids) > 0: self._reset_actors(env_ids) self._reset_env_tensors(env_ids) self._refresh_sim_tensors() self._compute_observations(env_ids) - return def _reset_env_tensors(self, env_ids): env_ids_int32 = self._humanoid_actor_ids[env_ids] @@ -210,13 +227,13 @@ def _setup_character_props(self, key_bodies): num_key_bodies = len(key_bodies) if (asset_file == "smplx/smplx_capsule.xml"): - self._dof_obs_size = (51)*3 - self._num_actions = (51)*3 + self._dof_obs_size = (51) * 3 + self._num_actions = (51) * 3 obj_obs_size = 15 - self._num_obs = 1 + (52) * (3 + 6 + 3 + 3) - 3 + 10*3 + obj_obs_size + self.ref_hoi_obs_size + self._num_obs = 1 + (52) * (3 + 6 + 3 + 3) - 3 + 10 * 3 + obj_obs_size + self.ref_hoi_obs_size else: print("Unsupported character config file: {s}".format(asset_file)) - assert(False) + assert (False) return @@ -227,12 +244,13 @@ def _build_termination_heights(self): def get_num_amp_obs(self): return self.ref_hoi_obs_size - + def _create_envs(self, num_envs, spacing, num_per_row): lower = gymapi.Vec3(-spacing, -spacing, 0.0) upper = gymapi.Vec3(spacing, spacing, spacing) - asset_root = self.cfg["env"]["asset"]["assetRoot"] + rofunc_path = get_rofunc_path() + asset_root = os.path.join(rofunc_path, "simulator/assets") asset_file = self.cfg["env"]["asset"]["assetFileName"] asset_path = os.path.join(asset_root, asset_file) @@ -252,7 +270,7 @@ def _create_envs(self, num_envs, spacing, num_per_row): actuator_props = self.gym.get_asset_actuator_properties(humanoid_asset) motor_efforts = [prop.motor_effort for prop in actuator_props] - + # create force sensors at the feet right_foot_idx = self.gym.find_asset_rigid_body_index(humanoid_asset, "right_foot") left_foot_idx = self.gym.find_asset_rigid_body_index(humanoid_asset, "left_foot") @@ -275,8 +293,8 @@ def _create_envs(self, num_envs, spacing, num_per_row): self.dof_limits_upper = [] max_agg_bodies = self.num_humanoid_bodies + 2 - max_agg_shapes = self.num_humanoid_shapes + 2 - + max_agg_shapes = self.num_humanoid_shapes + 2 + for i in range(self.num_envs): # create env instance env_ptr = self.gym.create_env(self.sim, lower, upper, num_per_row) @@ -303,7 +321,7 @@ def _create_envs(self, num_envs, spacing, num_per_row): self._build_pd_action_offset_scale() return - + def _build_env(self, env_id, env_ptr, humanoid_asset): col_group = env_id col_filter = self._get_humanoid_collision_filter() @@ -316,7 +334,8 @@ def _build_env(self, env_id, env_ptr, humanoid_asset): start_pose.p = gymapi.Vec3(*get_axis_params(char_h, self.up_axis_idx)) start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0) - humanoid_handle = self.gym.create_actor(env_ptr, humanoid_asset, start_pose, "humanoid", col_group, col_filter, segmentation_id) + humanoid_handle = self.gym.create_actor(env_ptr, humanoid_asset, start_pose, "humanoid", col_group, col_filter, + segmentation_id) self.gym.enable_actor_dof_force_sensors(env_ptr, humanoid_handle) @@ -333,7 +352,7 @@ def _build_env(self, env_id, env_ptr, humanoid_asset): return def _build_pd_action_offset_scale(self): - + lim_low = self.dof_limits_lower.cpu().numpy() lim_high = self.dof_limits_upper.cpu().numpy() @@ -349,21 +368,24 @@ def _get_humanoid_collision_filter(self): def _compute_reward(self, actions): self.rew_buf[:] = compute_humanoid_reward( - self._curr_ref_obs, - self._curr_obs, - self._contact_forces, - self._tar_contact_forces, - len(self._key_body_ids), - self.reward_weights - ) + self._curr_ref_obs, + self._curr_obs, + self._contact_forces, + self._tar_contact_forces, + len(self._key_body_ids), + self.reward_weights + ) return - + def _compute_reset(self): self.reset_buf[:], self._terminate_buf[:] = compute_humanoid_reset(self.reset_buf, self.progress_buf, - self._contact_forces, - self._rigid_body_pos, self.max_episode_length, - self._enable_early_termination, self._termination_heights, self._curr_ref_obs, self._curr_obs, - ) + self._contact_forces, + self._rigid_body_pos, + self.max_episode_length, + self._enable_early_termination, + self._termination_heights, + self._curr_ref_obs, self._curr_obs, + ) return def _refresh_sim_tensors(self): @@ -376,7 +398,6 @@ def _refresh_sim_tensors(self): self.gym.refresh_net_contact_force_tensor(self.sim) return - def _compute_task_obs(self, env_ids=None): if (env_ids is None): root_states = self._humanoid_root_states @@ -384,10 +405,10 @@ def _compute_task_obs(self, env_ids=None): else: root_states = self._humanoid_root_states[env_ids] tar_states = self._target_states[env_ids] - + obs = compute_obj_observations(root_states, tar_states) return obs - + def _compute_observations(self, env_ids=None): obs = self._compute_humanoid_obs(env_ids) @@ -397,18 +418,17 @@ def _compute_observations(self, env_ids=None): if (env_ids is None): ts = self.progress_buf.clone() - self._curr_ref_obs = self.hoi_data_dict[0]['hoi_data'][ts].clone() - next_ts = torch.clamp(ts + 1, max=self.max_episode_length-1) + self._curr_ref_obs = self.hoi_data_dict[0]['hoi_data'][ts].clone() + next_ts = torch.clamp(ts + 1, max=self.max_episode_length - 1) ref_obs = self.hoi_data_dict[0]['hoi_data'][next_ts].clone() - self.obs_buf[:] = torch.cat((obs,ref_obs),dim=-1) + self.obs_buf[:] = torch.cat((obs, ref_obs), dim=-1) else: ts = self.progress_buf[env_ids].clone() self._curr_ref_obs[env_ids] = self.hoi_data_dict[0]['hoi_data'][ts].clone() - next_ts = torch.clamp(ts + 1, max=self.max_episode_length-1) + next_ts = torch.clamp(ts + 1, max=self.max_episode_length - 1) ref_obs = self.hoi_data_dict[0]['hoi_data'][next_ts].clone() - self.obs_buf[env_ids] = torch.cat((obs,ref_obs),dim=-1) - + self.obs_buf[env_ids] = torch.cat((obs, ref_obs), dim=-1) return @@ -425,7 +445,7 @@ def _compute_humanoid_obs(self, env_ids=None): body_vel = self._rigid_body_vel[env_ids] body_ang_vel = self._rigid_body_ang_vel[env_ids] contact_forces = self._contact_forces[env_ids] - + obs = compute_humanoid_observations_max(body_pos, body_rot, body_vel, body_ang_vel, self._local_root_obs, self._root_height_obs, contact_forces, self._contact_body_ids) @@ -433,8 +453,8 @@ def _compute_humanoid_obs(self, env_ids=None): return obs def _reset_actors(self, env_ids): - self._humanoid_root_states[env_ids] = self._initial_humanoid_root_states[env_ids] - self._dof_pos[env_ids] = self._initial_dof_pos[env_ids] + self._humanoid_root_states[env_ids] = self._initial_humanoid_root_states[env_ids] + self._dof_pos[env_ids] = self._initial_dof_pos[env_ids] self._dof_vel[env_ids] = self._initial_dof_vel[env_ids] return @@ -447,22 +467,28 @@ def pre_physics_step(self, actions): else: forces = self.actions * self.motor_efforts.unsqueeze(0) * self.power_scale force_tensor = gymtorch.unwrap_tensor(forces) - self.gym.set_dof_actuation_force_tensor(self.sim, force_tensor) + self.gym.set_dof_actuation_force_tensor(self.sim, force_tensor) return def post_physics_step(self): self.progress_buf += 1 + env_ids = self.reset_buf.nonzero(as_tuple=False).flatten() + if len(env_ids) > 0: + self.reset_idx(env_ids) + self._refresh_sim_tensors() - #extra calc of self._curr_hoi_obs_buf, for correct calculate of imitation reward + # extra calc of self._curr_hoi_obs_buf, for correct calculate of imitation reward self._compute_hoi_observations() self._compute_observations() self._compute_reward(self.actions) self._compute_reset() - + + + self.extras["terminate"] = self._terminate_buf # debug viz @@ -485,7 +511,7 @@ def _build_key_body_ids_tensor(self, key_body_names): for body_name in key_body_names: body_id = self.gym.find_actor_rigid_body_handle(env_ptr, actor_handle, body_name) - assert(body_id != -1) + assert (body_id != -1) body_ids.append(body_id) body_ids = to_torch(body_ids, device=self.device, dtype=torch.long) @@ -498,7 +524,7 @@ def _build_contact_body_ids_tensor(self, contact_body_names): for body_name in contact_body_names: body_id = self.gym.find_actor_rigid_body_handle(env_ptr, actor_handle, body_name) - assert(body_id != -1) + assert (body_id != -1) body_ids.append(body_id) body_ids = to_torch(body_ids, device=self.device, dtype=torch.long) @@ -511,9 +537,9 @@ def _action_to_pd_targets(self, action): def _init_camera(self): self.gym.refresh_actor_root_state_tensor(self.sim) self._cam_prev_char_pos = self._humanoid_root_states[0, 0:3].cpu().numpy() - - cam_pos = gymapi.Vec3(self._cam_prev_char_pos[0], - self._cam_prev_char_pos[1] - 3.0, + + cam_pos = gymapi.Vec3(self._cam_prev_char_pos[0], + self._cam_prev_char_pos[1] - 3.0, 1.0) cam_target = gymapi.Vec3(self._cam_prev_char_pos[0], self._cam_prev_char_pos[1], @@ -524,14 +550,14 @@ def _init_camera(self): def _update_camera(self): self.gym.refresh_actor_root_state_tensor(self.sim) char_root_pos = self._humanoid_root_states[0, 0:3].cpu().numpy() - + cam_trans = self.gym.get_viewer_camera_transform(self.viewer, None) cam_pos = np.array([cam_trans.p.x, cam_trans.p.y, cam_trans.p.z]) cam_delta = cam_pos - self._cam_prev_char_pos new_cam_target = gymapi.Vec3(char_root_pos[0], char_root_pos[1], 1.0) - new_cam_pos = gymapi.Vec3(char_root_pos[0] + cam_delta[0], - char_root_pos[1] + cam_delta[1], + new_cam_pos = gymapi.Vec3(char_root_pos[0] + cam_delta[0], + char_root_pos[1] + cam_delta[1], cam_pos[2]) self.gym.viewer_camera_look_at(self.viewer, None, new_cam_pos, new_cam_target) @@ -543,23 +569,12 @@ def _update_camera(self): # new_cam_pos = gymapi.Vec3(1, -1, 1.6) # self.gym.viewer_camera_look_at(self.viewer, None, new_cam_pos, new_cam_target) return - def _update_debug_viz(self): self.gym.clear_lines(self.viewer) return - - - - - - - - - - class PhysHOI_BallPlay(Humanoid_SMPLX): class StateInit(Enum): Default = 0 @@ -567,10 +582,14 @@ class StateInit(Enum): Random = 2 Hybrid = 3 - def __init__(self, cfg, sim_params, physics_engine, device_type, device_id, headless): + # def __init__(self, cfg, sim_params, physics_engine, device_type, device_id, headless): + def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, + force_render): + state_init = cfg["env"]["stateInit"] self._state_init = PhysHOI_BallPlay.StateInit[state_init] self._hybrid_init_prob = cfg["env"]["hybridInitProb"] + self.rl_device = rl_device self._reset_default_env_ids = [] self._reset_ref_env_ids = [] @@ -582,15 +601,38 @@ def __init__(self, cfg, sim_params, physics_engine, device_type, device_id, head self.save_images = cfg['env']['saveImages'] self.init_vel = cfg['env']['initVel'] self.ball_size = cfg['env']['ballSize'] - - + + self.sim_params = self.__parse_sim_params( + cfg["physics_engine"], cfg["sim"] + ) + if cfg["physics_engine"] == "physx": + self.physics_engine = gymapi.SIM_PHYSX + elif cfg["physics_engine"] == "flex": + self.physics_engine = gymapi.SIM_FLEX + else: + msg = f"Invalid physics engine backend: {self.cfg['physics_engine']}" + raise ValueError(msg) + split_device = sim_device.split(":") + self.device_type = split_device[0] + self.device_id = int(split_device[1]) if len(split_device) > 1 else 0 + super().__init__(cfg=cfg, - sim_params=sim_params, - physics_engine=physics_engine, - device_type=device_type, - device_id=device_id, + sim_params=self.sim_params, + physics_engine=self.physics_engine, + device_type=self.device_type, + device_id=self.device_id, headless=headless) - + + if rf.oslab.is_absl_path(self.motion_file): + motion_file_path = self.motion_file + elif self.motion_file.split("/")[0] == "examples": + motion_file_path = os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "../../../../../../" + self.motion_file, + ) + else: + raise ValueError("Unsupported motion file path") + self.motion_file = motion_file_path self._load_motion(self.motion_file) self._curr_ref_obs = torch.zeros((self.num_envs, self.ref_hoi_obs_size), device=self.device, dtype=torch.float) @@ -606,12 +648,68 @@ def __init__(self, cfg, sim_params, physics_engine, device_type, device_id, head return + def __parse_sim_params( + self, physics_engine: str, config_sim + ) -> gymapi.SimParams: + """Parse the config dictionary for physics stepping settings. + + Args: + physics_engine: which physics engine to use. "physx" or "flex" + config_sim: dict of sim configuration parameters + Returns + IsaacGym SimParams object with updated settings. + """ + sim_params = gymapi.SimParams() + + # check correct up-axis + if config_sim["up_axis"] not in ["z", "y"]: + msg = f"Invalid physics up-axis: {config_sim['up_axis']}" + print(msg) + raise ValueError(msg) + + # assign general sim parameters + sim_params.dt = config_sim["dt"] + sim_params.num_client_threads = config_sim.get("num_client_threads", 0) + sim_params.use_gpu_pipeline = config_sim["use_gpu_pipeline"] + sim_params.substeps = config_sim.get("substeps", 2) + + # assign up-axis + if config_sim["up_axis"] == "z": + sim_params.up_axis = gymapi.UP_AXIS_Z + else: + sim_params.up_axis = gymapi.UP_AXIS_Y + + # assign gravity + sim_params.gravity = gymapi.Vec3(*config_sim["gravity"]) + + # configure physics parameters + if physics_engine == "physx": + # set the parameters + if "physx" in config_sim: + for opt in config_sim["physx"].keys(): + if opt == "contact_collection": + setattr( + sim_params.physx, + opt, + gymapi.ContactCollection(config_sim["physx"][opt]), + ) + else: + setattr(sim_params.physx, opt, config_sim["physx"][opt]) + else: + # set the parameters + if "flex" in config_sim: + for opt in config_sim["flex"].keys(): + setattr(sim_params.flex, opt, config_sim["flex"][opt]) + + # return the configured params + return sim_params + def post_physics_step(self): if self.projtype == "Mouse" or self.projtype == "Auto": self._update_proj() super().post_physics_step() - + self._update_hist_hoi_obs() self._compute_hoi_observations() @@ -620,12 +718,11 @@ def post_physics_step(self): def _update_hist_hoi_obs(self, env_ids=None): self._hist_obs = self._curr_obs.clone() return - + def _setup_character_props(self, key_bodies): super()._setup_character_props(key_bodies) return - def _load_motion(self, motion_file): # '''load HOI dataset''' @@ -640,60 +737,76 @@ def _load_motion(self, motion_file): # '''change the data framerate''' # NOTE: this is used for temporary testing, and is not rigorous that may yield incorrect rotations. dataFramesScale = self.cfg["env"]["dataFramesScale"] - scale_hoi_data = torch.nn.functional.interpolate(loaded_dict['hoi_data'].unsqueeze(1).transpose(0,2), scale_factor=dataFramesScale, mode='linear', align_corners=True) - loaded_dict['hoi_data'] = scale_hoi_data.transpose(0,2).squeeze(1).clone().contiguous() + scale_hoi_data = torch.nn.functional.interpolate(loaded_dict['hoi_data'].unsqueeze(1).transpose(0, 2), + scale_factor=dataFramesScale, mode='linear', + align_corners=True) + loaded_dict['hoi_data'] = scale_hoi_data.transpose(0, 2).squeeze(1).clone().contiguous() self.max_episode_length = loaded_dict['hoi_data'].shape[0] self.fps_data = 30. # self.fps_data = self.cfg["env"]["dataFPS"]*dataFramesScale loaded_dict['root_pos'] = loaded_dict['hoi_data'][:, 0:3].clone() - loaded_dict['root_pos_vel'] = (loaded_dict['root_pos'][1:,:].clone() - loaded_dict['root_pos'][:-1,:].clone())*self.fps_data - loaded_dict['root_pos_vel'] = torch.cat((torch.zeros((1, loaded_dict['root_pos_vel'].shape[-1])).to('cuda'),loaded_dict['root_pos_vel']),dim=0) + loaded_dict['root_pos_vel'] = (loaded_dict['root_pos'][1:, :].clone() - loaded_dict['root_pos'][:-1, + :].clone()) * self.fps_data + loaded_dict['root_pos_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['root_pos_vel'].shape[-1])).to('cuda'), loaded_dict['root_pos_vel']), dim=0) loaded_dict['root_rot'] = loaded_dict['hoi_data'][:, 3:6].clone() loaded_dict['root_rot_data'] = loaded_dict['root_rot'].clone() - loaded_dict['root_rot_vel'] = (loaded_dict['root_rot'][1:,:].clone() - loaded_dict['root_rot'][:-1,:].clone())*self.fps_data - loaded_dict['root_rot_vel'] = torch.cat((torch.zeros((1, loaded_dict['root_rot_vel'].shape[-1])).to('cuda'),loaded_dict['root_rot_vel']),dim=0) + loaded_dict['root_rot_vel'] = (loaded_dict['root_rot'][1:, :].clone() - loaded_dict['root_rot'][:-1, + :].clone()) * self.fps_data + loaded_dict['root_rot_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['root_rot_vel'].shape[-1])).to('cuda'), loaded_dict['root_rot_vel']), dim=0) loaded_dict['root_rot'] = torch_utils.exp_map_to_quat(loaded_dict['root_rot']).clone() - loaded_dict['dof_pos'] = loaded_dict['hoi_data'][:, 9:9+153].clone() - loaded_dict['dof_pos_vel'] = (loaded_dict['dof_pos'][1:,:].clone() - loaded_dict['dof_pos'][:-1,:].clone())*self.fps_data - loaded_dict['dof_pos_vel'] = torch.cat((torch.zeros((1, loaded_dict['dof_pos_vel'].shape[-1])).to('cuda'),loaded_dict['dof_pos_vel']),dim=0) - - loaded_dict['body_pos'] = loaded_dict['hoi_data'][:, 162: 162+52*3].clone().view(self.max_episode_length,52,3) - loaded_dict['key_body_pos'] = loaded_dict['body_pos'][:, self._key_body_ids, :].view(self.max_episode_length,-1).clone() - loaded_dict['key_body_pos_vel'] = (loaded_dict['key_body_pos'][1:,:].clone() - loaded_dict['key_body_pos'][:-1,:].clone())*self.fps_data - loaded_dict['key_body_pos_vel'] = torch.cat((torch.zeros((1, loaded_dict['key_body_pos_vel'].shape[-1])).to('cuda'),loaded_dict['key_body_pos_vel']),dim=0) + loaded_dict['dof_pos'] = loaded_dict['hoi_data'][:, 9:9 + 153].clone() + loaded_dict['dof_pos_vel'] = (loaded_dict['dof_pos'][1:, :].clone() - loaded_dict['dof_pos'][:-1, + :].clone()) * self.fps_data + loaded_dict['dof_pos_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['dof_pos_vel'].shape[-1])).to('cuda'), loaded_dict['dof_pos_vel']), dim=0) + + loaded_dict['body_pos'] = loaded_dict['hoi_data'][:, 162: 162 + 52 * 3].clone().view(self.max_episode_length, + 52, 3) + loaded_dict['key_body_pos'] = loaded_dict['body_pos'][:, self._key_body_ids, :].view(self.max_episode_length, + -1).clone() + loaded_dict['key_body_pos_vel'] = (loaded_dict['key_body_pos'][1:, :].clone() - loaded_dict['key_body_pos'][:-1, + :].clone()) * self.fps_data + loaded_dict['key_body_pos_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['key_body_pos_vel'].shape[-1])).to('cuda'), loaded_dict['key_body_pos_vel']), + dim=0) loaded_dict['obj_pos'] = loaded_dict['hoi_data'][:, 318:321].clone() - loaded_dict['obj_pos_vel'] = (loaded_dict['obj_pos'][1:,:].clone() - loaded_dict['obj_pos'][:-1,:].clone())*self.fps_data + loaded_dict['obj_pos_vel'] = (loaded_dict['obj_pos'][1:, :].clone() - loaded_dict['obj_pos'][:-1, + :].clone()) * self.fps_data if self.init_vel: - loaded_dict['obj_pos_vel'] = torch.cat((loaded_dict['obj_pos_vel'][:1],loaded_dict['obj_pos_vel']),dim=0) + loaded_dict['obj_pos_vel'] = torch.cat((loaded_dict['obj_pos_vel'][:1], loaded_dict['obj_pos_vel']), dim=0) else: - loaded_dict['obj_pos_vel'] = torch.cat((torch.zeros((1, loaded_dict['obj_pos_vel'].shape[-1])).to('cuda'),loaded_dict['obj_pos_vel']),dim=0) - + loaded_dict['obj_pos_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['obj_pos_vel'].shape[-1])).to('cuda'), loaded_dict['obj_pos_vel']), dim=0) loaded_dict['obj_rot'] = -loaded_dict['hoi_data'][:, 321:324].clone() - loaded_dict['obj_rot_vel'] = (loaded_dict['obj_rot'][1:,:].clone() - loaded_dict['obj_rot'][:-1,:].clone())*self.fps_data - loaded_dict['obj_rot_vel'] = torch.cat((torch.zeros((1, loaded_dict['obj_rot_vel'].shape[-1])).to('cuda'),loaded_dict['obj_rot_vel']),dim=0) + loaded_dict['obj_rot_vel'] = (loaded_dict['obj_rot'][1:, :].clone() - loaded_dict['obj_rot'][:-1, + :].clone()) * self.fps_data + loaded_dict['obj_rot_vel'] = torch.cat( + (torch.zeros((1, loaded_dict['obj_rot_vel'].shape[-1])).to('cuda'), loaded_dict['obj_rot_vel']), dim=0) loaded_dict['obj_rot'] = torch_utils.exp_map_to_quat(-loaded_dict['hoi_data'][:, 321:324]).clone() loaded_dict['contact'] = torch.round(loaded_dict['hoi_data'][:, 330:331].clone()) loaded_dict['hoi_data'] = torch.cat(( - loaded_dict['root_pos'].clone(), - loaded_dict['root_rot'].clone(), - loaded_dict['dof_pos'].clone(), - loaded_dict['dof_pos_vel'].clone(), - loaded_dict['obj_pos'].clone(), - loaded_dict['obj_rot'].clone(), - loaded_dict['obj_pos_vel'].clone(), - loaded_dict['key_body_pos'][:,:].clone(), - loaded_dict['contact'].clone() - ),dim=-1) - - assert(self.ref_hoi_obs_size == loaded_dict['hoi_data'].shape[-1]) + loaded_dict['root_pos'].clone(), + loaded_dict['root_rot'].clone(), + loaded_dict['dof_pos'].clone(), + loaded_dict['dof_pos_vel'].clone(), + loaded_dict['obj_pos'].clone(), + loaded_dict['obj_rot'].clone(), + loaded_dict['obj_pos_vel'].clone(), + loaded_dict['key_body_pos'][:, :].clone(), + loaded_dict['contact'].clone() + ), dim=-1) + + assert (self.ref_hoi_obs_size == loaded_dict['hoi_data'].shape[-1]) self.hoi_data_dict[0] = loaded_dict @@ -701,14 +814,15 @@ def _load_motion(self, motion_file): def _update_marker(self): - self._marker_states[:, :3] = self.hoi_data_dict[0]['obj_pos'][self.progress_buf,:]#.clone() - self._marker_states[:, 3:7] = self.hoi_data_dict[0]['obj_rot'][self.progress_buf,:]#.clone() #rand_rot - self._marker_states[:, 7:10] = self.hoi_data_dict[0]['obj_pos_vel'][self.progress_buf,:]#.clone() - self._marker_states[:, 10:13] = self.hoi_data_dict[0]['obj_rot_vel'][self.progress_buf,:]#.clone() + self._marker_states[:, :3] = self.hoi_data_dict[0]['obj_pos'][self.progress_buf, :] # .clone() + self._marker_states[:, 3:7] = self.hoi_data_dict[0]['obj_rot'][self.progress_buf, :] # .clone() #rand_rot + self._marker_states[:, 7:10] = self.hoi_data_dict[0]['obj_pos_vel'][self.progress_buf, :] # .clone() + self._marker_states[:, 10:13] = self.hoi_data_dict[0]['obj_rot_vel'][self.progress_buf, :] # .clone() self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._root_states), - gymtorch.unwrap_tensor(self._marker_actor_ids), len(self._marker_actor_ids)) - return + gymtorch.unwrap_tensor(self._marker_actor_ids), + len(self._marker_actor_ids)) + return def _create_envs(self, num_envs, spacing, num_per_row): @@ -730,7 +844,7 @@ def _build_env(self, env_id, env_ptr, humanoid_asset): if self.projtype == "Mouse" or self.projtype == "Auto": self._build_proj(env_id, env_ptr) - return + return def _build_proj(self, env_id, env_ptr): col_group = env_id @@ -747,12 +861,13 @@ def _build_proj(self, env_id, env_ptr): elif (obj_type == "large"): proj_asset = self._large_proj_asset - proj_handle = self.gym.create_actor(env_ptr, proj_asset, default_pose, "proj{:d}".format(i), col_group, col_filter, segmentation_id) + proj_handle = self.gym.create_actor(env_ptr, proj_asset, default_pose, "proj{:d}".format(i), col_group, + col_filter, segmentation_id) self._proj_handles.append(proj_handle) self.gym.set_actor_scale(env_ptr, proj_handle, 1) return - + def _build_proj_tensors(self): self._proj_dist_min = 4 self._proj_dist_max = 5 @@ -765,27 +880,30 @@ def _build_proj_tensors(self): num_actors = self.get_num_actors_per_env() num_objs = len(PERTURB_OBJS) - self._proj_states = self._root_states.view(self.num_envs, num_actors, self._root_states.shape[-1])[..., (num_actors - num_objs):, :] - + self._proj_states = self._root_states.view(self.num_envs, num_actors, self._root_states.shape[-1])[..., + (num_actors - num_objs):, :] + self._proj_actor_ids = num_actors * np.arange(self.num_envs) self._proj_actor_ids = np.expand_dims(self._proj_actor_ids, axis=-1) - self._proj_actor_ids = self._proj_actor_ids + np.reshape(np.array(self._proj_handles), [self.num_envs, num_objs]) + self._proj_actor_ids = self._proj_actor_ids + np.reshape(np.array(self._proj_handles), + [self.num_envs, num_objs]) self._proj_actor_ids = self._proj_actor_ids.flatten() self._proj_actor_ids = to_torch(self._proj_actor_ids, device=self.device, dtype=torch.int32) - + bodies_per_env = self._rigid_body_state.shape[0] // self.num_envs contact_force_tensor = self.gym.acquire_net_contact_force_tensor(self.sim) contact_force_tensor = gymtorch.wrap_tensor(contact_force_tensor) - self._proj_contact_forces = contact_force_tensor.view(self.num_envs, bodies_per_env, 3)[..., (num_actors - num_objs):, :] + self._proj_contact_forces = contact_force_tensor.view(self.num_envs, bodies_per_env, 3)[..., + (num_actors - num_objs):, :] self._calc_perturb_times() self.gym.subscribe_viewer_keyboard_event(self.viewer, gymapi.KEY_SPACE, "space_shoot") self.gym.subscribe_viewer_keyboard_event(self.viewer, gymapi.KEY_R, "reset") self.gym.subscribe_viewer_mouse_event(self.viewer, gymapi.MOUSE_LEFT_BUTTON, "mouse_shoot") - + return - + def _load_proj_asset(self): asset_root = "physhoi/data/assets/mjcf/" @@ -801,7 +919,7 @@ def _load_proj_asset(self): self._small_proj_asset = self.gym.load_asset(self.sim, asset_root, small_asset_file, small_asset_options) return - + def _load_marker_asset(self): asset_root = "physhoi/data/assets/mjcf/" asset_file = "location_marker.urdf" @@ -817,10 +935,11 @@ def _load_marker_asset(self): self._marker_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options) return - - def _load_target_asset(self): # smplx - asset_root = "physhoi/data/assets/mjcf/" - asset_file = "ball.urdf" + + def _load_target_asset(self): # smplx + rofunc_path = get_rofunc_path() + asset_root = os.path.join(rofunc_path, "simulator/assets") + asset_file = "mjcf/basketball/ball.urdf" asset_options = gymapi.AssetOptions() asset_options.angular_damping = 0.01 @@ -846,17 +965,17 @@ def _build_target(self, env_id, env_ptr): default_pose = gymapi.Transform() - target_handle = self.gym.create_actor(env_ptr, self._target_asset, default_pose, "target", col_group, col_filter, segmentation_id) + target_handle = self.gym.create_actor(env_ptr, self._target_asset, default_pose, "target", col_group, + col_filter, segmentation_id) # set ball color if self.cfg["headless"] == False: self.gym.set_rigid_body_color(env_ptr, target_handle, 0, gymapi.MESH_VISUAL, - gymapi.Vec3(1.5, 1.5, 1.5)) - # gymapi.Vec3(0., 1.0, 1.5)) + gymapi.Vec3(1.5, 1.5, 1.5)) + # gymapi.Vec3(0., 1.0, 1.5)) h = self.gym.create_texture_from_file(self.sim, 'physhoi/data/assets/mjcf/basketball.png') self.gym.set_rigid_body_texture(env_ptr, target_handle, 0, gymapi.MESH_VISUAL, h) - self._target_handles.append(target_handle) self.gym.set_actor_scale(env_ptr, target_handle, self.ball_size) @@ -868,58 +987,64 @@ def _build_marker(self, env_id, env_ptr): segmentation_id = 0 default_pose = gymapi.Transform() - - marker_handle = self.gym.create_actor(env_ptr, self._marker_asset, default_pose, "marker", col_group, col_filter, segmentation_id) + + marker_handle = self.gym.create_actor(env_ptr, self._marker_asset, default_pose, "marker", col_group, + col_filter, segmentation_id) self.gym.set_rigid_body_color(env_ptr, marker_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.8, 0.0, 0.0)) self._marker_handles.append(marker_handle) return - + def _build_target_tensors(self): num_actors = self.get_num_actors_per_env() self._target_states = self._root_states.view(self.num_envs, num_actors, self._root_states.shape[-1])[..., 1, :] - + self._tar_actor_ids = to_torch(num_actors * np.arange(self.num_envs), device=self.device, dtype=torch.int32) + 1 - + bodies_per_env = self._rigid_body_state.shape[0] // self.num_envs contact_force_tensor = self.gym.acquire_net_contact_force_tensor(self.sim) contact_force_tensor = gymtorch.wrap_tensor(contact_force_tensor) self._tar_contact_forces = contact_force_tensor.view(self.num_envs, bodies_per_env, 3)[..., self.num_bodies, :] return - + def _build_marker_state_tensors(self): num_actors = self._root_states.shape[0] // self.num_envs self._marker_states = self._root_states.view(self.num_envs, num_actors, self._root_states.shape[-1])[..., 2, :] self._marker_pos = self._marker_states[..., :3] - self._marker_actor_ids = to_torch(num_actors * np.arange(self.num_envs), device=self.device, dtype=torch.int32) + 2 + self._marker_actor_ids = to_torch(num_actors * np.arange(self.num_envs), device=self.device, + dtype=torch.int32) + 2 return def _reset_target(self, env_ids): - self._target_states[env_ids, :3] = self.hoi_data_dict[0]['obj_pos'][self.motion_times,:]#.clone()+0.5 - self._target_states[env_ids, 3:7] = self.hoi_data_dict[0]['obj_rot'][self.motion_times,:]#.clone() #rand_rot - self._target_states[env_ids, 7:10] = self.hoi_data_dict[0]['obj_pos_vel'][self.motion_times,:]#.clone() - self._target_states[env_ids, 10:13] = self.hoi_data_dict[0]['obj_rot_vel'][self.motion_times,:]#.clone() + self._target_states[env_ids, :3] = self.hoi_data_dict[0]['obj_pos'][self.motion_times, :] # .clone()+0.5 + self._target_states[env_ids, 3:7] = self.hoi_data_dict[0]['obj_rot'][self.motion_times, :] # .clone() #rand_rot + self._target_states[env_ids, 7:10] = self.hoi_data_dict[0]['obj_pos_vel'][self.motion_times, :] # .clone() + self._target_states[env_ids, 10:13] = self.hoi_data_dict[0]['obj_rot_vel'][self.motion_times, :] # .clone() return - - + def _reset_env_tensors(self, env_ids): super()._reset_env_tensors(env_ids) - env_ids_int32 = self._tar_actor_ids[env_ids] self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._root_states), - gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32)) - + gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32)) + return - def _reset_envs(self, env_ids): + # def _reset_envs(self, env_ids): + # self._reset_default_env_ids = [] + # self._reset_ref_env_ids = [] + # + # super()._reset_envs(env_ids) + # + # return + + def reset_idx(self, env_ids): self._reset_default_env_ids = [] self._reset_ref_env_ids = [] - super()._reset_envs(env_ids) - - return + super().reset_idx(env_ids) def _reset_actors(self, env_ids): if (self._state_init == PhysHOI_BallPlay.StateInit.Default): @@ -930,14 +1055,12 @@ def _reset_actors(self, env_ids): elif (self._state_init == PhysHOI_BallPlay.StateInit.Hybrid): self._reset_hybrid_state_init(env_ids) else: - assert(False), "Unsupported state initialization strategy: {:s}".format(str(self._state_init)) + assert (False), "Unsupported state initialization strategy: {:s}".format(str(self._state_init)) self._reset_target(env_ids) return - - def _reset_default(self, env_ids): self._humanoid_root_states[env_ids] = self._initial_humanoid_root_states[env_ids] self._dof_pos[env_ids] = self._initial_dof_pos[env_ids] @@ -949,27 +1072,28 @@ def _reset_ref_state_init(self, env_ids): num_envs = env_ids.shape[0] if (self._state_init == PhysHOI_BallPlay.StateInit.Random - or self._state_init == PhysHOI_BallPlay.StateInit.Hybrid): - motion_times = torch.randint(0, self.hoi_data_dict[0]['hoi_data'].shape[0]-2, (num_envs,), device=self.device, dtype=torch.long) + or self._state_init == PhysHOI_BallPlay.StateInit.Hybrid): + motion_times = torch.randint(0, self.hoi_data_dict[0]['hoi_data'].shape[0] - 2, (num_envs,), + device=self.device, dtype=torch.long) elif (self._state_init == PhysHOI_BallPlay.StateInit.Start): - motion_times = torch.zeros(num_envs, device=self.device, dtype=torch.long)#.int() + motion_times = torch.zeros(num_envs, device=self.device, dtype=torch.long) # .int() self.motion_times = motion_times.clone() - #TODO: i should has shape of env_ids - i = random.randint(0, self.num_motions-1) + # TODO: i should has shape of env_ids + i = random.randint(0, self.num_motions - 1) - self._set_env_state(env_ids=env_ids, - root_pos=self.hoi_data_dict[i]['root_pos'][motion_times,:].clone(), - root_rot=self.hoi_data_dict[i]['root_rot'][motion_times,:].clone(), - dof_pos=self.hoi_data_dict[i]['dof_pos'][motion_times,:].clone(), - root_vel=self.hoi_data_dict[i]['root_pos_vel'][motion_times,:].clone(), - root_ang_vel=self.hoi_data_dict[i]['root_rot_vel'][motion_times,:].clone(), - dof_vel=self.hoi_data_dict[i]['dof_pos_vel'][motion_times,:].clone(), - ) + self._set_env_state(env_ids=env_ids, + root_pos=self.hoi_data_dict[i]['root_pos'][motion_times, :].clone(), + root_rot=self.hoi_data_dict[i]['root_rot'][motion_times, :].clone(), + dof_pos=self.hoi_data_dict[i]['dof_pos'][motion_times, :].clone(), + root_vel=self.hoi_data_dict[i]['root_pos_vel'][motion_times, :].clone(), + root_ang_vel=self.hoi_data_dict[i]['root_rot_vel'][motion_times, :].clone(), + dof_vel=self.hoi_data_dict[i]['dof_pos_vel'][motion_times, :].clone(), + ) return - + def _reset_hybrid_state_init(self, env_ids): num_envs = env_ids.shape[0] ref_probs = to_torch(np.array([self._hybrid_init_prob] * num_envs), device=self.device) @@ -985,43 +1109,42 @@ def _reset_hybrid_state_init(self, env_ids): return - def _set_env_state(self, env_ids, root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel): self._humanoid_root_states[env_ids, 0:3] = root_pos self._humanoid_root_states[env_ids, 3:7] = root_rot self._humanoid_root_states[env_ids, 7:10] = root_vel self._humanoid_root_states[env_ids, 10:13] = root_ang_vel - + self._dof_pos[env_ids] = dof_pos self._dof_vel[env_ids] = dof_vel return - def _compute_hoi_observations(self, env_ids=None): key_body_pos = self._rigid_body_pos[:, self._key_body_ids, :] ## diffvel, set 0 for the first frame - hist_dof_pos = self._hist_obs[:,7:7+153] - dof_diffvel = (self._dof_pos - hist_dof_pos)*self.fps_data - dof_diffvel = dof_diffvel*(self.progress_buf!=1).to(float).unsqueeze(dim=-1) + hist_dof_pos = self._hist_obs[:, 7:7 + 153] + dof_diffvel = (self._dof_pos - hist_dof_pos) * self.fps_data + dof_diffvel = dof_diffvel * (self.progress_buf != 1).to(float).unsqueeze(dim=-1) if (env_ids is None): self._curr_obs[:] = build_hoi_observations(self._rigid_body_pos[:, 0, :], - self._rigid_body_rot[:, 0, :], - self._rigid_body_vel[:, 0, :], - self._rigid_body_ang_vel[:, 0, :], - self._dof_pos, self._dof_vel, key_body_pos, - self._local_root_obs, self._root_height_obs, - self._dof_obs_size, self._target_states, - dof_diffvel) + self._rigid_body_rot[:, 0, :], + self._rigid_body_vel[:, 0, :], + self._rigid_body_ang_vel[:, 0, :], + self._dof_pos, self._dof_vel, key_body_pos, + self._local_root_obs, self._root_height_obs, + self._dof_obs_size, self._target_states, + dof_diffvel) else: self._curr_obs[env_ids] = build_hoi_observations(self._rigid_body_pos[env_ids][:, 0, :], - self._rigid_body_rot[env_ids][:, 0, :], - self._rigid_body_vel[env_ids][:, 0, :], - self._rigid_body_ang_vel[env_ids][:, 0, :], - self._dof_pos[env_ids], self._dof_vel[env_ids], key_body_pos[env_ids], - self._local_root_obs, self._root_height_obs, - self._dof_obs_size, self._target_states[env_ids], - dof_diffvel[env_ids]) + self._rigid_body_rot[env_ids][:, 0, :], + self._rigid_body_vel[env_ids][:, 0, :], + self._rigid_body_ang_vel[env_ids][:, 0, :], + self._dof_pos[env_ids], self._dof_vel[env_ids], + key_body_pos[env_ids], + self._local_root_obs, self._root_height_obs, + self._dof_obs_size, self._target_states[env_ids], + dof_diffvel[env_ids]) return def _calc_perturb_times(self): @@ -1035,14 +1158,14 @@ def _calc_perturb_times(self): self._perturb_timesteps = np.array(self._perturb_timesteps) return - + def _update_proj(self): if self.projtype == 'Auto': curr_timestep = self.progress_buf.cpu().numpy()[0] curr_timestep = curr_timestep % (self._perturb_timesteps[-1] + 1) perturb_step = np.where(self._perturb_timesteps == curr_timestep)[0] - + if (len(perturb_step) > 0): perturb_id = perturb_step[0] n = self.num_envs @@ -1050,17 +1173,19 @@ def _update_proj(self): rand_theta = torch.rand([n], dtype=self._proj_states.dtype, device=self._proj_states.device) rand_theta *= 2 * np.pi - rand_dist = (self._proj_dist_max - self._proj_dist_min) * torch.rand([n], dtype=self._proj_states.dtype, device=self._proj_states.device) + self._proj_dist_min + rand_dist = (self._proj_dist_max - self._proj_dist_min) * torch.rand([n], dtype=self._proj_states.dtype, + device=self._proj_states.device) + self._proj_dist_min pos_x = rand_dist * torch.cos(rand_theta) pos_y = -rand_dist * torch.sin(rand_theta) - pos_z = (self._proj_h_max - self._proj_h_min) * torch.rand([n], dtype=self._proj_states.dtype, device=self._proj_states.device) + self._proj_h_min - + pos_z = (self._proj_h_max - self._proj_h_min) * torch.rand([n], dtype=self._proj_states.dtype, + device=self._proj_states.device) + self._proj_h_min + self._proj_states[..., perturb_id, 0] = humanoid_root_pos[..., 0] + pos_x self._proj_states[..., perturb_id, 1] = humanoid_root_pos[..., 1] + pos_y self._proj_states[..., perturb_id, 2] = pos_z self._proj_states[..., perturb_id, 3:6] = 0.0 self._proj_states[..., perturb_id, 6] = 1.0 - + tar_body_idx = np.random.randint(self.num_bodies) tar_body_idx = 1 @@ -1068,7 +1193,8 @@ def _update_proj(self): launch_dir = launch_tar_pos - self._proj_states[..., perturb_id, 0:3] launch_dir += 0.1 * torch.randn_like(launch_dir) launch_dir = torch.nn.functional.normalize(launch_dir, dim=-1) - launch_speed = (self._proj_speed_max - self._proj_speed_min) * torch.rand_like(launch_dir[:, 0:1]) + self._proj_speed_min + launch_speed = (self._proj_speed_max - self._proj_speed_min) * torch.rand_like( + launch_dir[:, 0:1]) + self._proj_speed_min launch_vel = launch_speed * launch_dir launch_vel[..., 0:2] += self._rigid_body_vel[..., tar_body_idx, 0:2] self._proj_states[..., perturb_id, 7:10] = launch_vel @@ -1077,7 +1203,7 @@ def _update_proj(self): self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._root_states), gymtorch.unwrap_tensor(self._proj_actor_ids), len(self._proj_actor_ids)) - + elif self.projtype == 'Mouse': # mouse control for evt in self.gym.query_viewer_action_events(self.viewer): @@ -1110,33 +1236,33 @@ def _update_proj(self): self._proj_states[..., 9] = vel.z self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._root_states), - gymtorch.unwrap_tensor(self._proj_actor_ids), - len(self._proj_actor_ids)) + gymtorch.unwrap_tensor(self._proj_actor_ids), + len(self._proj_actor_ids)) return - + def play_dataset_step(self, time): t = time ### update object ### - self._target_states[:, :3] = self.hoi_data_dict[0]['obj_pos'][t,:] - self._target_states[:, 3:7] = self.hoi_data_dict[0]['obj_rot'][t,:] + self._target_states[:, :3] = self.hoi_data_dict[0]['obj_pos'][t, :] + self._target_states[:, 3:7] = self.hoi_data_dict[0]['obj_rot'][t, :] self._target_states[:, 7:10] = torch.zeros_like(self._target_states[:, 7:10]) self._target_states[:, 10:13] = torch.zeros_like(self._target_states[:, 10:13]) ### update subject ### - _humanoid_root_pos = self.hoi_data_dict[0]['root_pos'][t,:].clone() - _humanoid_root_rot = self.hoi_data_dict[0]['root_rot'][t,:].clone() + _humanoid_root_pos = self.hoi_data_dict[0]['root_pos'][t, :].clone() + _humanoid_root_rot = self.hoi_data_dict[0]['root_rot'][t, :].clone() self._humanoid_root_states[:, 0:3] = _humanoid_root_pos self._humanoid_root_states[:, 3:7] = _humanoid_root_rot self._humanoid_root_states[:, 7:10] = torch.zeros_like(self._humanoid_root_states[:, 7:10]) self._humanoid_root_states[:, 10:13] = torch.zeros_like(self._humanoid_root_states[:, 10:13]) - - self._dof_pos[:] = self.hoi_data_dict[0]['dof_pos'][t,:].clone() + + self._dof_pos[:] = self.hoi_data_dict[0]['dof_pos'][t, :].clone() self._dof_vel[:] = torch.zeros_like(self._dof_vel[:]) - contact = self.hoi_data_dict[0]['contact'][t,:] + contact = self.hoi_data_dict[0]['contact'][t, :] self.gym.set_actor_root_state_tensor(self.sim, gymtorch.unwrap_tensor(self._root_states)) self.gym.set_dof_state_tensor(self.sim, gymtorch.unwrap_tensor(self._dof_state)) @@ -1150,20 +1276,42 @@ def play_dataset_step(self, time): if obj_contact == True: self.gym.set_rigid_body_color(env_ptr, handle, 0, gymapi.MESH_VISUAL, - gymapi.Vec3(1., 0., 0.)) + gymapi.Vec3(1., 0., 0.)) else: self.gym.set_rigid_body_color(env_ptr, handle, 0, gymapi.MESH_VISUAL, - gymapi.Vec3(0., 1., 0.)) - + gymapi.Vec3(0., 1., 0.)) + self.render(t=t) self.gym.simulate(self.sim) - return - - def _draw_task_play(self,t): + def get_dataset_step(self, time): + t = time + + ### update object ### + self._target_states[:, :3] = self.hoi_data_dict[0]['obj_pos'][t, :] + self._target_states[:, 3:7] = self.hoi_data_dict[0]['obj_rot'][t, :] + self._target_states[:, 7:10] = torch.zeros_like(self._target_states[:, 7:10]) + self._target_states[:, 10:13] = torch.zeros_like(self._target_states[:, 10:13]) + + ### update subject ### + _humanoid_root_pos = self.hoi_data_dict[0]['root_pos'][t, :].clone() + _humanoid_root_rot = self.hoi_data_dict[0]['root_rot'][t, :].clone() + self._humanoid_root_states[:, 0:3] = _humanoid_root_pos + self._humanoid_root_states[:, 3:7] = _humanoid_root_rot + self._humanoid_root_states[:, 7:10] = torch.zeros_like(self._humanoid_root_states[:, 7:10]) + self._humanoid_root_states[:, 10:13] = torch.zeros_like(self._humanoid_root_states[:, 10:13]) + + self._dof_pos[:] = self.hoi_data_dict[0]['dof_pos'][t, :].clone() + self._dof_vel[:] = torch.zeros_like(self._dof_vel[:]) + + contact = self.hoi_data_dict[0]['contact'][t, :] + + return self._dof_state + + def _draw_task_play(self, t): # self._update_marker() - - cols = np.array([[1.0, 0.0, 0.0]], dtype=np.float32) # color + + cols = np.array([[1.0, 0.0, 0.0]], dtype=np.float32) # color self.gym.clear_lines(self.viewer) @@ -1171,7 +1319,7 @@ def _draw_task_play(self,t): for i, env_ptr in enumerate(self.envs): for j in range(len(self._key_body_ids)): - vec = self.hoi_data_dict[0]['key_body_pos'][t, j*3:j*3+3] + vec = self.hoi_data_dict[0]['key_body_pos'][t, j * 3:j * 3 + 3] vec = torch.cat([starts, vec], dim=-1).cpu().numpy().reshape([1, 6]) self.gym.add_lines(self.viewer, env_ptr, 1, vec, cols) @@ -1182,7 +1330,7 @@ def render(self, sync_frame_time=False, t=0): if self.viewer: self._draw_task() - + if self.save_images: env_ids = 0 if self.play_dataset: @@ -1192,9 +1340,9 @@ def render(self, sync_frame_time=False, t=0): dataname = self.motion_file[len('physhoi/data/motions/BallPlay/'):-3] rgb_filename = "physhoi/data/images/" + dataname + "/rgb_env%d_frame%05d.png" % (env_ids, frame_id) os.makedirs("physhoi/data/images/" + dataname, exist_ok=True) - self.gym.write_viewer_image_to_file(self.viewer,rgb_filename) + self.gym.write_viewer_image_to_file(self.viewer, rgb_filename) return - + def _draw_task(self): # self._update_marker() @@ -1214,24 +1362,17 @@ def _draw_task(self): return - - - - - - - ##################################################################### ###=========================jit functions=========================### ##################################################################### # @torch.jit.script -def build_hoi_observations(root_pos, root_rot, root_vel, root_ang_vel, dof_pos, dof_vel, key_body_pos, +def build_hoi_observations(root_pos, root_rot, root_vel, root_ang_vel, dof_pos, dof_vel, key_body_pos, local_root_obs, root_height_obs, dof_obs_size, target_states, dof_diffvel): - - - contact = torch.zeros(key_body_pos.shape[0],1).cuda() - obs = torch.cat((root_pos, root_rot, dof_pos, dof_diffvel, target_states[:,:10], key_body_pos.contiguous().view(-1,key_body_pos.shape[1]*key_body_pos.shape[2]), contact), dim=-1) + contact = torch.zeros(key_body_pos.shape[0], 1).cuda() + obs = torch.cat((root_pos, root_rot, dof_pos, dof_diffvel, target_states[:, :10], + key_body_pos.contiguous().view(-1, key_body_pos.shape[1] * key_body_pos.shape[2]), contact), + dim=-1) return obs @@ -1247,7 +1388,7 @@ def compute_obj_observations(root_states, tar_states): tar_ang_vel = tar_states[:, 10:13] heading_rot = torch_utils.calc_heading_quat_inv(root_rot) - + local_tar_pos = tar_pos - root_pos local_tar_pos[..., -1] = tar_pos[..., -1] local_tar_pos = quat_rotate(heading_rot, local_tar_pos) @@ -1262,36 +1403,40 @@ def compute_obj_observations(root_states, tar_states): @torch.jit.script -def compute_humanoid_observations_max(body_pos, body_rot, body_vel, body_ang_vel, local_root_obs, root_height_obs, contact_forces, contact_body_ids): +def compute_humanoid_observations_max(body_pos, body_rot, body_vel, body_ang_vel, local_root_obs, root_height_obs, + contact_forces, contact_body_ids): # type: (Tensor, Tensor, Tensor, Tensor, bool, bool, Tensor, Tensor) -> Tensor root_pos = body_pos[:, 0, :] root_rot = body_rot[:, 0, :] root_h = root_pos[:, 2:3] heading_rot = torch_utils.calc_heading_quat_inv(root_rot) - + if (not root_height_obs): root_h_obs = torch.zeros_like(root_h) else: root_h_obs = root_h - + heading_rot_expand = heading_rot.unsqueeze(-2) heading_rot_expand = heading_rot_expand.repeat((1, body_pos.shape[1], 1)) - flat_heading_rot = heading_rot_expand.reshape(heading_rot_expand.shape[0] * heading_rot_expand.shape[1], - heading_rot_expand.shape[2]) - + flat_heading_rot = heading_rot_expand.reshape(heading_rot_expand.shape[0] * heading_rot_expand.shape[1], + heading_rot_expand.shape[2]) + root_pos_expand = root_pos.unsqueeze(-2) local_body_pos = body_pos - root_pos_expand - flat_local_body_pos = local_body_pos.reshape(local_body_pos.shape[0] * local_body_pos.shape[1], local_body_pos.shape[2]) + flat_local_body_pos = local_body_pos.reshape(local_body_pos.shape[0] * local_body_pos.shape[1], + local_body_pos.shape[2]) flat_local_body_pos = quat_rotate(flat_heading_rot, flat_local_body_pos) - local_body_pos = flat_local_body_pos.reshape(local_body_pos.shape[0], local_body_pos.shape[1] * local_body_pos.shape[2]) - local_body_pos = local_body_pos[..., 3:] # remove root pos + local_body_pos = flat_local_body_pos.reshape(local_body_pos.shape[0], + local_body_pos.shape[1] * local_body_pos.shape[2]) + local_body_pos = local_body_pos[..., 3:] # remove root pos flat_body_rot = body_rot.reshape(body_rot.shape[0] * body_rot.shape[1], body_rot.shape[2]) flat_local_body_rot = quat_mul(flat_heading_rot, flat_body_rot) flat_local_body_rot_obs = torch_utils.quat_to_tan_norm(flat_local_body_rot) - local_body_rot_obs = flat_local_body_rot_obs.reshape(body_rot.shape[0], body_rot.shape[1] * flat_local_body_rot_obs.shape[1]) - + local_body_rot_obs = flat_local_body_rot_obs.reshape(body_rot.shape[0], + body_rot.shape[1] * flat_local_body_rot_obs.shape[1]) + if (local_root_obs): root_rot_obs = torch_utils.quat_to_tan_norm(root_rot) local_body_rot_obs[..., 0:6] = root_rot_obs @@ -1299,157 +1444,155 @@ def compute_humanoid_observations_max(body_pos, body_rot, body_vel, body_ang_vel flat_body_vel = body_vel.reshape(body_vel.shape[0] * body_vel.shape[1], body_vel.shape[2]) flat_local_body_vel = quat_rotate(flat_heading_rot, flat_body_vel) local_body_vel = flat_local_body_vel.reshape(body_vel.shape[0], body_vel.shape[1] * body_vel.shape[2]) - + flat_body_ang_vel = body_ang_vel.reshape(body_ang_vel.shape[0] * body_ang_vel.shape[1], body_ang_vel.shape[2]) flat_local_body_ang_vel = quat_rotate(flat_heading_rot, flat_body_ang_vel) - local_body_ang_vel = flat_local_body_ang_vel.reshape(body_ang_vel.shape[0], body_ang_vel.shape[1] * body_ang_vel.shape[2]) - - body_contact_buf = contact_forces[:, contact_body_ids, :].clone().view(contact_forces.shape[0],-1) - - obs = torch.cat((root_h_obs, local_body_pos, local_body_rot_obs, local_body_vel, local_body_ang_vel, body_contact_buf), dim=-1) - return obs + local_body_ang_vel = flat_local_body_ang_vel.reshape(body_ang_vel.shape[0], + body_ang_vel.shape[1] * body_ang_vel.shape[2]) + body_contact_buf = contact_forces[:, contact_body_ids, :].clone().view(contact_forces.shape[0], -1) + obs = torch.cat( + (root_h_obs, local_body_pos, local_body_rot_obs, local_body_vel, local_body_ang_vel, body_contact_buf), dim=-1) + return obs # @torch.jit.script def compute_humanoid_reward(hoi_ref, hoi_obs, contact_buf, tar_contact_forces, len_keypos, w): - ## type: (Tensor, Tensor, Tensor, Tensor, Int, float) -> Tensor + # type: (Tensor, Tensor, Tensor, Tensor, Int, float) -> Tensor ### data preprocess ### # simulated states - root_pos = hoi_obs[:,:3] - root_rot = hoi_obs[:,3:3+4] - dof_pos = hoi_obs[:,7:7+51*3] - dof_pos_vel = hoi_obs[:,160:160+51*3] - obj_pos = hoi_obs[:,313:313+3] - obj_rot = hoi_obs[:,316:316+4] - obj_pos_vel = hoi_obs[:,320:320+3] - key_pos = hoi_obs[:,323:323+len_keypos*3] - contact = hoi_obs[:,-1:]# fake one - key_pos = torch.cat((root_pos, key_pos),dim=-1) - body_rot = torch.cat((root_rot, dof_pos),dim=-1) - ig = key_pos.view(-1,len_keypos+1,3).transpose(0,1) - obj_pos[:,:3] - ig = ig.transpose(0,1).view(-1,(len_keypos+1)*3) + root_pos = hoi_obs[:, :3] + root_rot = hoi_obs[:, 3:3 + 4] + dof_pos = hoi_obs[:, 7:7 + 51 * 3] + dof_pos_vel = hoi_obs[:, 160:160 + 51 * 3] + obj_pos = hoi_obs[:, 313:313 + 3] + obj_rot = hoi_obs[:, 316:316 + 4] + obj_pos_vel = hoi_obs[:, 320:320 + 3] + key_pos = hoi_obs[:, 323:323 + len_keypos * 3] + contact = hoi_obs[:, -1:] # fake one + key_pos = torch.cat((root_pos, key_pos), dim=-1) + body_rot = torch.cat((root_rot, dof_pos), dim=-1) + ig = key_pos.view(-1, len_keypos + 1, 3).transpose(0, 1) - obj_pos[:, :3] + ig = ig.transpose(0, 1).view(-1, (len_keypos + 1) * 3) # reference states - ref_root_pos = hoi_ref[:,:3] - ref_root_rot = hoi_ref[:,3:3+4] - ref_dof_pos = hoi_ref[:,7:7+51*3] - ref_dof_pos_vel = hoi_ref[:,160:160+51*3] - ref_obj_pos = hoi_ref[:,313:313+3] - ref_obj_rot = hoi_ref[:,316:316+4] - ref_obj_pos_vel = hoi_ref[:,320:320+3] - ref_key_pos = hoi_ref[:,323:323+len_keypos*3] - ref_obj_contact = hoi_ref[:,-1:] - ref_key_pos = torch.cat((ref_root_pos, ref_key_pos),dim=-1) - ref_body_rot = torch.cat((ref_root_rot, ref_dof_pos),dim=-1) - ref_ig = ref_key_pos.view(-1,len_keypos+1,3).transpose(0,1) - ref_obj_pos[:,:3] - ref_ig = ref_ig.transpose(0,1).view(-1,(len_keypos+1)*3) - + ref_root_pos = hoi_ref[:, :3] + ref_root_rot = hoi_ref[:, 3:3 + 4] + ref_dof_pos = hoi_ref[:, 7:7 + 51 * 3] + ref_dof_pos_vel = hoi_ref[:, 160:160 + 51 * 3] + ref_obj_pos = hoi_ref[:, 313:313 + 3] + ref_obj_rot = hoi_ref[:, 316:316 + 4] + ref_obj_pos_vel = hoi_ref[:, 320:320 + 3] + ref_key_pos = hoi_ref[:, 323:323 + len_keypos * 3] + ref_obj_contact = hoi_ref[:, -1:] + ref_key_pos = torch.cat((ref_root_pos, ref_key_pos), dim=-1) + ref_body_rot = torch.cat((ref_root_rot, ref_dof_pos), dim=-1) + ref_ig = ref_key_pos.view(-1, len_keypos + 1, 3).transpose(0, 1) - ref_obj_pos[:, :3] + ref_ig = ref_ig.transpose(0, 1).view(-1, (len_keypos + 1) * 3) ### body reward ### # body pos reward - ep = torch.mean((ref_key_pos - key_pos)**2,dim=-1) - rp = torch.exp(-ep*w['p']) + ep = torch.mean((ref_key_pos - key_pos) ** 2, dim=-1) + rp = torch.exp(-ep * w['p']) # body rot reward - er = torch.mean((ref_body_rot - body_rot)**2,dim=-1) - rr = torch.exp(-er*w['r']) + er = torch.mean((ref_body_rot - body_rot) ** 2, dim=-1) + rr = torch.exp(-er * w['r']) # body pos vel reward epv = torch.zeros_like(ep) - rpv = torch.exp(-epv*w['pv']) + rpv = torch.exp(-epv * w['pv']) # body rot vel reward - erv = torch.mean((ref_dof_pos_vel - dof_pos_vel)**2,dim=-1) - rrv = torch.exp(-erv*w['rv']) - - rb = rp*rr*rpv*rrv + erv = torch.mean((ref_dof_pos_vel - dof_pos_vel) ** 2, dim=-1) + rrv = torch.exp(-erv * w['rv']) + rb = rp * rr * rpv * rrv ### object reward ### # object pos reward - eop = torch.mean((ref_obj_pos - obj_pos)**2,dim=-1) - rop = torch.exp(-eop*w['op']) + eop = torch.mean((ref_obj_pos - obj_pos) ** 2, dim=-1) + rop = torch.exp(-eop * w['op']) # object rot reward - eor = torch.zeros_like(ep) #torch.mean((ref_obj_rot - obj_rot)**2,dim=-1) - ror = torch.exp(-eor*w['or']) + eor = torch.zeros_like(ep) # torch.mean((ref_obj_rot - obj_rot)**2,dim=-1) + ror = torch.exp(-eor * w['or']) # object pos vel reward - eopv = torch.mean((ref_obj_pos_vel - obj_pos_vel)**2,dim=-1) - ropv = torch.exp(-eopv*w['opv']) + eopv = torch.mean((ref_obj_pos_vel - obj_pos_vel) ** 2, dim=-1) + ropv = torch.exp(-eopv * w['opv']) # object rot vel reward - eorv = torch.zeros_like(ep) #torch.mean((ref_obj_rot_vel - obj_rot_vel)**2,dim=-1) - rorv = torch.exp(-eorv*w['orv']) - - ro = rop*ror*ropv*rorv + eorv = torch.zeros_like(ep) # torch.mean((ref_obj_rot_vel - obj_rot_vel)**2,dim=-1) + rorv = torch.exp(-eorv * w['orv']) + ro = rop * ror * ropv * rorv ### interaction graph reward ### - eig = torch.mean((ref_ig - ig)**2,dim=-1) - rig = torch.exp(-eig*w['ig']) - + eig = torch.mean((ref_ig - ig) ** 2, dim=-1) + rig = torch.exp(-eig * w['ig']) ### simplified contact graph reward ### - # Since Isaac Gym does not yet provide API for detailed collision detection in GPU pipeline, + # Since Isaac Gym does not yet provide API for detailed collision detection in GPU pipeline, # we use force detection to approximate the contact status. # In this case we use the CG node istead of the CG edge for imitation. # TODO: update the code once collision detection API is available. ## body ids - # Pelvis, 0 - # L_Hip, 1 + # Pelvis, 0 + # L_Hip, 1 # L_Knee, 2 # L_Ankle, 3 # L_Toe, 4 - # R_Hip, 5 + # R_Hip, 5 # R_Knee, 6 # R_Ankle, 7 # R_Toe, 8 # Torso, 9 - # Spine, 10 + # Spine, 10 # Chest, 11 # Neck, 12 # Head, 13 - # L_Thorax, 14 + # L_Thorax, 14 # L_Shoulder, 15 # L_Elbow, 16 # L_Wrist, 17 # L_Hand, 18-32 - # R_Thorax, 33 + # R_Thorax, 33 # R_Shoulder, 34 # R_Elbow, 35 - # R_Wrist, 36 + # R_Wrist, 36 # R_Hand, 37-51 # body contact - contact_body_ids = [0,1,2,5,6,9,10,11,12,13,14,15,16,33,34,35] + contact_body_ids = [0, 1, 2, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 33, 34, 35] body_contact_buf = contact_buf[:, contact_body_ids, :].clone() body_contact = torch.all(torch.abs(body_contact_buf) < 0.1, dim=-1) - body_contact = torch.all(body_contact, dim=-1).to(float) # =1 when no contact happens to the body + body_contact = torch.all(body_contact, dim=-1).to(float) # =1 when no contact happens to the body # object contact - obj_contact = torch.any(torch.abs(tar_contact_forces[..., 0:2]) > 0.1, dim=-1).to(float) # =1 when contact happens to the object - - ref_body_contact = torch.ones_like(ref_obj_contact) # no body contact for all time - ecg1 = torch.abs(body_contact - ref_body_contact[:,0]) - rcg1 = torch.exp(-ecg1*w['cg1']) - ecg2 = torch.abs(obj_contact - ref_obj_contact[:,0]) - rcg2 = torch.exp(-ecg2*w['cg2']) + obj_contact = torch.any(torch.abs(tar_contact_forces[..., 0:2]) > 0.1, dim=-1).to( + float) # =1 when contact happens to the object - rcg = rcg1*rcg2 + ref_body_contact = torch.ones_like(ref_obj_contact) # no body contact for all time + ecg1 = torch.abs(body_contact - ref_body_contact[:, 0]) + rcg1 = torch.exp(-ecg1 * w['cg1']) + ecg2 = torch.abs(obj_contact - ref_obj_contact[:, 0]) + rcg2 = torch.exp(-ecg2 * w['cg2']) + rcg = rcg1 * rcg2 ### task-agnostic HOI imitation reward ### - reward = rb*ro*rig*rcg + reward = rb * ro * rig * rcg + + # reward = 1 return reward @@ -1461,13 +1604,13 @@ def compute_humanoid_reset(reset_buf, progress_buf, contact_buf, rigid_body_pos, terminated = torch.zeros_like(reset_buf) if (enable_early_termination): - body_height = rigid_body_pos[:, 0, 2] # root height - body_fall = body_height < termination_heights# [4096] + body_height = rigid_body_pos[:, 0, 2] # root height + body_fall = body_height < termination_heights # [4096] has_failed = body_fall.clone() has_failed *= (progress_buf > 1) - + terminated = torch.where(has_failed, torch.ones_like(reset_buf), terminated) - reset = torch.where(progress_buf >= max_episode_length-1, torch.ones_like(reset_buf), terminated) + reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), terminated) - return reset, terminated \ No newline at end of file + return reset, terminated diff --git a/rofunc/learning/RofuncRL/trainers/__init__.py b/rofunc/learning/RofuncRL/trainers/__init__.py index 044d3f242..e75417104 100644 --- a/rofunc/learning/RofuncRL/trainers/__init__.py +++ b/rofunc/learning/RofuncRL/trainers/__init__.py @@ -8,6 +8,7 @@ def __init__(self): from .ase_trainer import ASETrainer from .dtrans_trainer import DTransTrainer from .hotu_trainer import HOTUTrainer + from .physhoi_trainer import PhysHOITrainer self.trainer_map = { "ppo": PPOTrainer, @@ -18,6 +19,7 @@ def __init__(self): "ase": ASETrainer, "dtrans": DTransTrainer, "hotu": HOTUTrainer, + "physhoi": PhysHOITrainer, } def __call__(self, trainer_name): diff --git a/rofunc/learning/RofuncRL/trainers/base_trainer.py b/rofunc/learning/RofuncRL/trainers/base_trainer.py index 3f62cd859..ba6cbfab7 100644 --- a/rofunc/learning/RofuncRL/trainers/base_trainer.py +++ b/rofunc/learning/RofuncRL/trainers/base_trainer.py @@ -103,8 +103,9 @@ def __init__(self, self.eval_steps = self.cfg.train.Trainer.get("eval_steps", 1000) self.eval_env_seed = self.cfg.train.Trainer.get("eval_env_seed", random.randint(0, 10000)) self.use_eval_thread = self.cfg.train.Trainer.get("use_eval_thread", False) - assert self.eval_steps % self.max_episode_steps == 0, \ - f"eval_steps ({self.eval_steps}) must be a multiple of max_episode_steps ({self.max_episode_steps})." + if self.eval_flag: + assert self.eval_steps % self.max_episode_steps == 0, \ + f"eval_steps ({self.eval_steps}) must be a multiple of max_episode_steps ({self.max_episode_steps})." self.inference_steps = self.cfg.train.Trainer.get("inference_steps", 1000) self.total_rew_mean = -1e4 self.eval_rew_mean = 0 diff --git a/rofunc/learning/RofuncRL/trainers/physhoi_trainer.py b/rofunc/learning/RofuncRL/trainers/physhoi_trainer.py new file mode 100644 index 000000000..21322f594 --- /dev/null +++ b/rofunc/learning/RofuncRL/trainers/physhoi_trainer.py @@ -0,0 +1,101 @@ +# Copyright 2023, Junjia LIU, jjliu@mae.cuhk.edu.hk +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import tqdm + +from rofunc.learning.RofuncRL.agents.mixline.physhoi_agent import PhysHOIAgent +from rofunc.learning.RofuncRL.trainers.base_trainer import BaseTrainer +from rofunc.learning.RofuncRL.utils.memory import RandomMemory + + +class PhysHOITrainer(BaseTrainer): + def __init__(self, cfg, env, device, env_name, **kwargs): + super().__init__(cfg, env, device, env_name, **kwargs) + self.collect_observation = lambda: self.env.reset_done()[0]["obs"] + self.memory = RandomMemory(memory_size=self.rollouts, num_envs=self.env.num_envs, device=device) + self.agent = PhysHOIAgent(cfg.train, self.env.observation_space, self.env.action_space, self.memory, + device, self.exp_dir, self.rofunc_logger) + + def pre_interaction(self): + if self.collect_observation is not None: + self.agent._current_states = self.collect_observation() + + # def get_action(self, states): + # if self._step < self.random_steps: + # actions = torch.tensor([self.env.action_space.sample() for _ in range(self.env.num_envs)]).to(self.device) + # else: + # actions, _ = self.agent.act(states, deterministic=True) + # return actions + + def post_interaction(self): + self._rollout += 1 + + # Update agent + if not self._rollout % self.rollouts and self._step >= self.start_learning_steps: + self.agent.update_net() + self._update_times += 1 + self.rofunc_logger.info(f'Update {self._update_times} times.', local_verbose=False) + + super().post_interaction() + + def inference(self): + states, infos = self.env.reset() + + if self.cfg.task.env.playdataset: + # # play dataset + # while True: + # for t in range(self.env.max_episode_length): + # self.env.play_dataset_step(t) + # # reset env + # if self.env.terminated.any() or self.env.truncated.any(): + t = 0 + for _ in tqdm.trange(self.inference_steps): + self.pre_interaction() + with torch.no_grad(): + # Obtain action from agent + action_state = self.env.get_dataset_step(t) + action_pos = action_state[:, 0] + action_pos = action_pos.repeat(self.env.num_envs, 1) + t += 1 + + # Interact with environment + next_states, rewards, terminated, truncated, infos = self.env.step(action_pos) + + # # Reset the environment + if t >= self.env.max_episode_length: + states, infos = self.env.reset() + + # if terminated.any() or truncated.any(): + # states, infos = self.env.reset() + # else: + # states = next_states.clone() + else: + for _ in tqdm.trange(self.inference_steps): + self.pre_interaction() + with torch.no_grad(): + # Obtain action from agent + actions, _ = self.agent.act(states, deterministic=True) # TODO: check + + # Interact with environment + next_states, rewards, terminated, truncated, infos = self.env.step(actions) + + # Reset the environment + if terminated.any() or truncated.any(): + states, infos = self.env.reset() + else: + states = next_states.clone() + # close the environment + self.env.close() + self.rofunc_logger.info('Inference complete.') diff --git a/rofunc/learning/RofuncRL/utils/memory.py b/rofunc/learning/RofuncRL/utils/memory.py index 28aa42937..7d98a13d4 100644 --- a/rofunc/learning/RofuncRL/utils/memory.py +++ b/rofunc/learning/RofuncRL/utils/memory.py @@ -201,7 +201,7 @@ def create_tensor(self, # define tensor shape tensor_shape = (self.memory_size, self.num_envs, *size) if keep_dimensions else ( - self.memory_size, self.num_envs, size) + self.memory_size, self.num_envs, size) view_shape = (-1, *size) if keep_dimensions else (-1, size) # create tensor (_tensor_) and add it to the internal storage setattr(self, "_tensor_{}".format(name), torch.zeros(tensor_shape, device=self.device, dtype=dtype)) @@ -268,8 +268,12 @@ def add_samples(self, **tensors: torch.Tensor) -> None: if name in self.tensors: self.tensors[name][self.memory_index].copy_(tensor) except: - raise ValueError("The tensors have incompatible shapes. \n name: {} \n Expect shape: {} " - "\n Got shape: {}".format(name, self.tensors[name][self.memory_index].shape, tensor.shape)) + try: + raise ValueError("The tensors have incompatible shapes. \n name: {} \n Expect shape: {} " + "\n Got shape: {}".format(name, self.tensors[name][self.memory_index].shape, + tensor.shape)) + except: + raise ValueError("The tensor might be None. \n name: {}".format(name)) self.memory_index += 1 # multi environment (number of environments less than num_envs) elif dim >= 2 and shape[0] < self.num_envs: diff --git a/rofunc/learning/pre_trained_models/download.py b/rofunc/learning/pre_trained_models/download.py index 0de798e6d..bcd2d571a 100644 --- a/rofunc/learning/pre_trained_models/download.py +++ b/rofunc/learning/pre_trained_models/download.py @@ -22,7 +22,7 @@ def download_ckpt(url, name, md5=None): beauty_print("Downloading pretrained model {}".format(name), type="info") ckpt_path = os.path.join(get_rofunc_path(), 'learning/pre_trained_models/{}'.format(name)) - gdown.cached_download(url, ckpt_path, md5=md5) + gdown.cached_download(url, ckpt_path) return ckpt_path diff --git a/rofunc/simulator/assets/mjcf/basketball/ball.urdf b/rofunc/simulator/assets/mjcf/basketball/ball.urdf new file mode 100644 index 000000000..b25c5a135 --- /dev/null +++ b/rofunc/simulator/assets/mjcf/basketball/ball.urdf @@ -0,0 +1,23 @@ + + + + + + + + + + + + + + + + + + + + + + + diff --git a/rofunc/simulator/assets/mjcf/basketball/basketball.png b/rofunc/simulator/assets/mjcf/basketball/basketball.png new file mode 100644 index 000000000..db6902bfe Binary files /dev/null and b/rofunc/simulator/assets/mjcf/basketball/basketball.png differ diff --git a/rofunc/simulator/assets/mjcf/objects/meshes/lab_box.obj b/rofunc/simulator/assets/mjcf/objects/meshes/lab_box.obj index d5487af76..e93c1622f 100755 --- a/rofunc/simulator/assets/mjcf/objects/meshes/lab_box.obj +++ b/rofunc/simulator/assets/mjcf/objects/meshes/lab_box.obj @@ -9,3838 +9,3838 @@ # Faces: 3868 # #### -vn 0.564328 -0.256459 6.221416 -v 0.584380 0.011061 0.202900 -vn -0.228808 0.210413 6.264717 -v 0.581818 0.017500 0.202900 -vn 1.029641 2.757063 3.856423 -v 0.565527 0.017500 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+f 11602 11603 11604 diff --git a/rofunc/simulator/assets/smplx/smplx_capsule.xml b/rofunc/simulator/assets/smplx/smplx_capsule.xml new file mode 100644 index 000000000..0b9a804e4 --- /dev/null +++ b/rofunc/simulator/assets/smplx/smplx_capsule.xml @@ -0,0 +1,495 @@ + + + + diff --git a/rofunc/simulator/assets/urdf/ycb b/rofunc/simulator/assets/urdf/ycb new file mode 120000 index 000000000..dd3ac20eb --- /dev/null +++ b/rofunc/simulator/assets/urdf/ycb @@ -0,0 +1 @@ +/home/ubuntu/Github/Rofunc/rofunc/simulator/assets/urdf/ycb \ No newline at end of file diff --git a/rofunc/simulator/src/convex_decomposition.py b/rofunc/simulator/src/convex_decomposition.py index 8c8f5ea88..c45da7a89 100644 --- a/rofunc/simulator/src/convex_decomposition.py +++ b/rofunc/simulator/src/convex_decomposition.py @@ -14,6 +14,7 @@ from isaacgym import gymapi from isaacgym import gymutil +import rofunc as rf # initialize gym gym = gymapi.acquire_gym() @@ -65,7 +66,7 @@ initial_pose = gymapi.Transform() initial_pose.p = gymapi.Vec3(0.0, 0.0, 0.2) -asset_root = "../assets" +asset_root = rf.utils.get_rofunc_path() + "/simulator/assets" asset_options = gymapi.AssetOptions() # Load materials from meshes diff --git a/rofunc/utils/datalab/poselib/optitrack_fbx_to_hotu_npy.py b/rofunc/utils/datalab/poselib/optitrack_fbx_to_hotu_npy.py index 1c7d8a6d3..2c428d85f 100644 --- a/rofunc/utils/datalab/poselib/optitrack_fbx_to_hotu_npy.py +++ b/rofunc/utils/datalab/poselib/optitrack_fbx_to_hotu_npy.py @@ -239,6 +239,8 @@ def motion_retargeting(retarget_cfg, source_motion, visualize=False): root_translation = target_motion.root_translation local_rotation = local_rotation[frame_beg:frame_end, ...] root_translation = root_translation[frame_beg:frame_end, ...] + + ## move the human to the origin # avg_root_translation = root_translation.mean(axis=0) # root_translation[1:] -= avg_root_translation @@ -253,8 +255,10 @@ def motion_retargeting(retarget_cfg, source_motion, visualize=False): local_rotation = target_motion.local_rotation root_translation = target_motion.root_translation tar_global_pos = target_motion.global_translation - min_h = torch.min(tar_global_pos[..., 2]) - root_translation[:, 2] += -min_h + + ## Set the human foot on the ground + # min_h = torch.min(tar_global_pos[..., 2]) + # root_translation[:, 2] += -min_h # adjust the height of the root to avoid ground penetration root_height_offset = retarget_cfg["root_height_offset"] @@ -353,16 +357,17 @@ def npy_from_fbx(fbx_file): source_motion = motion_from_fbx(fbx_file, root_joint="Skeleton_Hips", fps=120, visualize=False) # config["target_motion_path"] = fbx_file.replace('.fbx', '_amp.npy') - motion_retargeting(config, source_motion, visualize=True) + motion_retargeting(config, source_motion, visualize=False) if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() - parser.add_argument("--fbx_dir", type=str, default=None) - parser.add_argument("--fbx_file", type=str, default="/home/ubuntu/Github/Rofunc/examples/data/hotu2/test_data_01_optitrack.fbx") - parser.add_argument("--parallel", action="store_true") + parser.add_argument("--fbx_dir", type=str, default="../../../../examples/data/hotu2") + # parser.add_argument("--fbx_file", type=str, default="../../../../examples/data/hotu2/test_data_01_optitrack.fbx") + parser.add_argument("--fbx_file", type=str, default="../../../../examples/data/hotu2/test_data_01_optitrack.fbx") + parser.add_argument("--parallel", action="store_false") args = parser.parse_args() rofunc_path = rf.oslab.get_rofunc_path() diff --git a/setup.py b/setup.py index 6e4dbd79b..1f8e03cb2 100644 --- a/setup.py +++ b/setup.py @@ -33,7 +33,7 @@ 'hydra-core==1.3.2', 'opencv-python==4.7.0.72', 'neurokit2==0.2.4', - 'gdown==4.6.3', + 'gdown==5.1.0', 'pytz==2023.3', 'shutup==0.2.0', 'numpy<=1.23.0',