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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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.. image:: ../../img/pipeline.png | ||
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Installation | ||
---- | ||
The installation is very easy, | ||
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.. code-block:: python | ||
pip install rofunc | ||
and as you'll find later, it's easy to use as well! | ||
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.. code-block:: python | ||
import rofunc as rf | ||
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 | | ||
+==================================+===============+==========================+=======================================================================+ | ||
| **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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# Release log |