This repo is an attempt to make a hexapod learn walking motion using Proximal Policy Optimisation and using Motion Capture data generated using a custom CPG controller. This project is inspired by
- Create a new python virtual env with python 3.6, 3.7 or 3.8 (3.8 recommended)
- Install pytorch 1.10 with cuda-11.3:
pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
- Install Isaac Gym
- Download and install Isaac Gym Preview 3 (Preview 2 will not work!) from https://developer.nvidia.com/isaac-gym
cd isaacgym/python && pip install -e .- Try running an example
cd examples && python 1080_balls_of_solitude.py - For troubleshooting check docs
isaacgym/docs/index.html)
- Install rsl_rl (PPO implementation)
- Clone https://github.com/leggedrobotics/rsl_rl
cd rsl_rl && pip install -e .
- Install legged_gym
- Clone this repository
cd legged_gym && pip install -e .
- To generate MoCap data, run
data_generator.py - To train yuna run
python3 train.py --task=yuna - To test the trained policy run
python3 test.py --task=yuna