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Residual Force Control (RFC)

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This repo contains the official implementation of our paper:

Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
Ye Yuan, Kris Kitani
NeurIPS 2020
[website] [paper] [video]

Installation

Dataset and Pretrained Models

  • The CMU mocap data we use is already included in the data/cmu_mocap folder. The pretrained models are in results/motion_im where each subfolder (e.g., 0506) corresponds to a training config file (e.g., 0506.yml) in motion_imitation/cfg.
  • We have provided the following configs (label): 0506 (ballet1), 0507 (ballet2), 0513 (ballet3), 8801 (backflip), 9002 (cartwheel), 9005 (jump kick), 9008 (side flip), 9011 (handspring).

Environment

  • Tested OS: MacOS, Linux
  • Python >= 3.6

How to install

  1. Install the dependencies:
    pip install -r requirements.txt
    
  2. Install MuJoCo following the steps here. Note that mujoco-py (MuJoCo's python binding) is already installed in step 1. This step is to install the actual MuJoCo library. You will need to apply for a MuJoCo Personal License (free for students).
  3. Set the following environment variable to improve multi-threaded sampling performance:
    export OMP_NUM_THREADS=1
    

Quick Demo

Visualize Pretrained Policy

python motion_imitation/vis_im.py --cfg 0506 --iter 1000

Here 0506 corresponds to the config motion_imitation/cfg/0506.yml which uses the motion_clip of subject 05 and trial 06 from the CMU mocap database. Check out motion_imitation/cfg for other configs.

Keyboard shortcuts for the GUI:

Key Functionality Key Functionality
space pause/resume animation w/e go to first/last frame
left/right previous/next frame d/f slow down/speed up
g toggle loop s toggle reverse

Training

Prepare training data

Note: for motion clips used in the paper, the data has already been processed.

To train policies with other motion clips from the CMU mocap database, you will need to first download the .amc mocap file and place it inside data/cmu_mocap/amc, and run the following script:

python motion_imitation/data_process/convert_cmu_mocap.py --amc_id 05_06 --out_id 05_06_test --render

Here we take 05_06.amc as an example and this command will show a GUI that visualizes the motion clip. The GUI has a few keyboard shortcuts that allow you to adjust the clip and save the training pickle file (05_06_test.p) inside data/cmu_mocap/motion:

Key Functionality Key Functionality
space pause/resume animation w/e go to first/last frame
left/right previous/next frame d/f slow down/speed up
up/down move humanoid up/down z/x set start/end of motion
c cut motion with selected range r save motion as pickle

If you don't need to adjust the clip, you can just run the script without --render, so the motion pickle is saved without GUI.

Train RFC policy

You can create your own config file using 0506.yml as a template. Be sure to set motion_id to the motion you have processed. To train an RFC policy, just run:

python motion_imitation/motion_im.py --cfg 0506 --num_threads <max_num_CPU_threads_you_have>

This will save models and logs into results/motion_im/0506.

Citation

If you find our work useful in your research, please cite our paper RFC:

@inproceedings{yuan2020residual,
  title={Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis},
  author={Yuan, Ye and Kitani, Kris},
  booktitle={Advances in Neural Information Processing Systems},
  year={2020}
}

License

The software in this repo is freely available for free non-commercial use. Please see the license for further details.

About

Official PyTorch Implementation of "Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis". NeurIPS 2020.

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