Hierarchical Locomotion and Path Planning

This repository contains code for training and evaluating hierarchical policies fo The code borrows the environment and robot constructions and is adapted from the code provided alongside 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 requirements: pip3 install -r requirements.txt

Training Models

To train a policy using a single thread, run the following command:

python3 motion_imitation/rltest.py --mode train --motion_file motion_imitation/data/motions/dog_pace.txt --visualize

• --mode can be either train, or test, path,sweep for evaluation and generation of log files for plotting.
• --motion_file specifies the reference motion that the robot uses for pose initialization. motion_imitation/data/motions/ contains different reference motion clips. Here we are not using the full motion, but only take frames for initialization if set.
• --visualize enables visualization, and rendering can be disabled by removing the flag.

For parallel training with Ray, run:

ray start --head --redis-port=6379 --num-cpus=X where X is the number of CPU's available

• --n_directions number of directions to test to obtain reward gradient
• --deltas_used number of best directions to update after each iteration
• --n_workers number of parallel workers for ARS learning
• --rollout_length number of steps per rollout (episode)
• --currsteps 0 to turn off curriculum lengthening or curriculum length
• --actionlim limit of motor angle corrections [0,1]
• --saveniters save weights and log data every n iterations