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| # Overview | ||
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| Rofunc package focuses on the **robotic Imitation Learning (IL) and Learning from Demonstration (LfD)** fields and provides valuable and | ||
| convenient python functions for robotics, including _demonstration collection, data pre-processing, LfD algorithms, planning, and control methods_. We also plan to provide an Isaac Gym-based robot simulator for evaluation. This package aims to advance the field by building a full-process toolkit and validation platform that simplifies and standardizes the process of demonstration data collection, processing, learning, and its deployment on robots. | ||
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| ## Installation | ||
| The installation is very easy, | ||
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| ``` | ||
| pip install rofunc | ||
| ``` | ||
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| and as you'll find later, it's easy to use as well! | ||
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| ```python | ||
| import rofunc as rf | ||
| ``` | ||
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| Thus, have fun in the robotics world! | ||
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| ## Available functions | ||
| Currently, we provide a simple document; please refer to [here](./rofunc/). A comprehensive one with both English and | ||
| Chinese versions is built via the [readthedoc](https://rofunc.readthedocs.io/en/stable/). | ||
| The available functions and plans can be found as follows. | ||
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| | Classes | Types | Functions | Description | Status | | ||
| |---------------------------------|--------------|-------------------------|----------------------------------------------------------------------|--------| | ||
| | **Devices** | Xsens | `xsens.record` | Record the human motion via network streaming | | | ||
| | | | `xsens.process` | Decode the .mvnx file | ✅ | | ||
| | | | `xsens.visualize` | Show or save gif about the motion | ✅ | | ||
| | | Optitrack | `optitrack.record` | Record the motion of markers via network streaming | | | ||
| | | | `optitrack.process` | Process the output .csv data | ✅ | | ||
| | | | `optitrack.visualize` | Show or save gif about the motion | | | ||
| | | ZED | `zed.record` | Record with multiple cameras | ✅ | | ||
| | | | `zed.playback` | Playback the recording and save snapshots | ✅ | | ||
| | | | `zed.export` | Export the recording to mp4 | ✅ | | ||
| | | Multimodal | `mmodal.record` | Record multi-modal demonstration data simultaneously | | | ||
| | | | `mmodal.export` | Export multi-modal demonstration data in one line | ✅ | | ||
| | **Learning from Demonstration** | DMP | `dmp.uni` | DMP for one agent with several (or one) demonstrated trajectories | | | ||
| | | GMR | `gmr.uni` | GMR for one agent with several (or one) demonstrated trajectories | ✅ | | ||
| | | TP-GMM | `tpgmm.uni` | TP-GMM for one agent with several (or one) demonstrated trajectories | ✅ | | ||
| | | | `tpgmm.bi` | TP-GMM for two agent with coordination learned from demonstration | ✅ | | ||
| | | TP-GMR | `tpgmr.uni` | TP-GMR for one agent with several (or one) demonstrated trajectories | ✅ | | ||
| | | | `tpgmr.bi` | TP-GMR for two agent with coordination learned from demonstration | ✅ | | ||
| | **Planning** | LQT | `lqt.uni` | LQT for one agent with several via-points | ✅ | | ||
| | | | `lqt.bi` | LQT for two agent with coordination constraints | ✅ | | ||
| | | | `lqt.recursive` | Generate smooth trajectories for robot execution recursively | ✅ | | ||
| | **Logger** | | `logger.write` | Custom tensorboard-based logger | | | ||
| | **Coordinate** | | `coord.custom_class` | Define the custom class of `Pose` | | | ||
| | | | `coord.transform` | Useful functions about coordinate transformation | ✅ | | ||
| | **VisuaLab** | Trajectory | `visualab.trajectory` | 2-dim/3-dim/with ori trajectory visualization | ✅ | | ||
| | | Distribution | `visualab.distribution` | 2-dim/3-dim distribution visualization | ✅ | | ||
| | | Ellipsoid | `visualab.ellipsoid` | 2-dim/3-dim ellipsoid visualization | ✅ | | ||
| | **RoboLab** | Kinematics | `robolab.kinematics` | ... | ✅ | | ||
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