Isaac Gym RL template environment for legged robots

This repository contains an Isaac Gym template environment that can be used to train any legged robot using rl_games. This repository is deployed with zero-shot sim-to-real transfer in the following projects:

• Text2Robot: a framework that converts user text specifications and performance preferences into physical quadrupedal robots.

  

• DukeHumanoidv1: an open-source 10-degrees-of-freedom child size humanoid for locomotion research.

  

Table of content

• project structure
• Setting up
  • Tested environment
  • Setup python virtual environment
  • Setup vscode
  • to start conda env
  • to train a policy
  • to run example checkpoint:

project structure

.
├── assets
│   ├── checkpoints # saved checkpoint
│   │   ├── A1Terrain.pth
│   │   └── ...
│   ├── joint_monkey.py # script to check the urdf
│   └── urdf
│       ├── a1
│       ├── anymal_c
│       └──...
├── envs
│   ├── cfg # stores all configurations
│   │   ├── config.yaml
│   │   ├── pbt
│   │   ├── task
│   │   └── train
│   ├── common # common scripts
│   │   ├── publisher.py
│   │   ├── terrain.py
│   │   └── utils.py
│   ├── exp.sh # experiment scripts
│   ├── __init__.py
│   ├── plot_juggler # plot_juggler configurations
│   │   └── robotdog7kg_play_debug.xml
│   ├── run.sh # run script
│   ├── setup # conda env setup script
│   │   ├── conda_env.yaml
│   │   └── ...
│   ├── tasks # 
│   │   ├── __init__.py
│   │   └── legged_terrain.py
│   └── train.py
├── outputs # contains trained results
└── README.md

Setting up

Tested environment

• Operating system: Ubuntu 22.04 with CUDA 12.3
• Development environment: Visual Studio Code (VSCode)
• Python environment management: micromanba

Setup python virtual environment

first install Micromamba if you have not done so. Recommended to install at ~/repo/micromamba

"${SHELL}" <(curl -L micro.mamba.pm/install.sh)

Setup a python virtual envirnpoment named  py38 with conda yaml file setup/conda_env.yaml

alias conda="micromamba"

# Create environment
conda env create --file setup/conda_env_py38.yaml -y

# Activate the environment
conda activate py38

# Export library path
export LD_LIBRARY_PATH=${CONDA_PREFIX}/lib

Setup vscode

Install VSCode: Download and install vscode if you have not done so

Install vscode Extensions:

• Python: ms-python.python Optionally Install other extensions such as Git based on your needs.

To configure the project using vscode:

• Open the project folder.
• Select the correct Python virtual environment.

to start conda env

Ensure you're operating within the Python virtual environment. For convenience, we'll assume all commands in this guide are executed within this activated environment:

conda activate py38 && export LD_LIBRARY_PATH=${CONDA_PREFIX}/lib

to train a policy

cd envs

# duke humanoid v1 baseline
bash run.sh dukehumanoid_baseline

# duke humanoid v1 passive policy
bash run.sh dukehumanoid_passive

# robot dog7kg
bash run.sh dog

# a1
bash run.sh a1Terrain

# anymal
bash run.sh anymalTerrain

to run example checkpoint:

cd envs

# duke humanoid v1 baseline
bash run.sh dukehumanoid_baseline -p
bash run.sh dukehumanoid_baseline -pk # with keyboard wasd

# duke humanoid v1 passive policy
bash run.sh dukehumanoid_passive -p

# robot dog7kg
bash run.sh dog -p
# a1
bash run.sh a1Terrain -p
# anymal
bash run.sh anymalTerrain -p

# dry run (print the command only)
bash run.sh anymalTerrain -d

Debugging

Use bash run.sh [THE_TASK_TO_RUN] -d to start a remote attache python debuging using debugpy with vscode, for example

bash run.sh dukehumanoid_baseline -d # train mode + debug
bash run.sh dukehumanoid_baseline -pd # play mode + debug
bash run.sh a1Terrain -pkd # play mode + keyboard + debug

Then go to the vscode debug panel (keyboard shortcut: ctrl+shift+D), in the RUN AND DEBUG, select option Python Debugger: Remote Attach to start debugging.