Motion Imitation

This repository contains code accompanying the paper:

"Learning Agile Robotic Locomotion Skills by Imitating Animals",

by Xue Bin Peng et al. It provides a Gym environment for training a simulated quadruped robot to imitate various reference motions, and example training code for learning the policies.

Project page: https://xbpeng.github.io/projects/Robotic_Imitation/index.html

Getting Started

Install dependencies:

• Install MPI: sudo apt install libopenmpi-dev
• Install requirements: pip3 install -r requirements.txt

and it should be good to go.

Training Models

To train a policy, run the following command:

python3 motion_imitation/run.py --mode train --motion_file motion_imitation/data/motions/dog_pace.txt --int_save_freq 10000000 --visualize

• --mode can be either train or test.
• --motion_file specifies the reference motion that the robot is to imitate. motion_imitation/data/motions/ contains different reference motion clips.
• --int_save_freq specifies the frequency for saving intermediate policies every n policy steps.
• --visualize enables visualization, and rendering can be disabled by removing the flag.
• the trained model and logs will be written to output/.

For parallel training with MPI run:

mpiexec -n 8 python3 motion_imitation/run.py --mode train --motion_file motion_imitation/data/motions/dog_pace.txt --int_save_freq 10000000

• -n is the number of parallel.

Testing Models

To test a trained model, run the following command

python3 motion_imitation/run.py --mode test --motion_file motion_imitation/data/motions/dog_pace.txt --model_file motion_imitation/data/policies/dog_pace.zip --visualize

• --model_file specifies the .zip file that contains the trained model. Pretrained models are available in motion_imitation/data/policies/.

Data

• motion_imitation/data/motions/ contains different reference motion clips.
• motion_imitation/data/policies/ contains pretrained models for the different reference motions.

For more information on the reference motion data format, see the DeepMimic documentation

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Disclaimer: This is not an official Google product.