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ABS

Official Implementation for Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion.

Robotics: Science and Systems (RSS) 2024

Tairan He*, Chong Zhang*, Wenli Xiao, Guanqi He, Changliu Liu, Guanya Shi

<p align="center"> <img src="images/Youtube-Cover[2M].png" width="80%"/> </p>

This codebase is under CC BY-NC 4.0 license, with inherited license in Legged Gym and RSL RL from ETH Zurich, Nikita Rudin and NVIDIA CORPORATION & AFFILIATES. You may not use the material for commercial purposes, e.g., to make demos to advertise your commercial products.

Please read through the whole README.md before cloning the repo.

Training in Simulation

Pipeline to Install and Train ABS

Note: Before running our code, it's highly recommended to first play with RSL's Legged Gym version to get a basic understanding of the Isaac-LeggedGym-RslRL framework.

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  1. Create environment and install torch

    conda create -n xxx python=3.8  # or use virtual environment/docker

pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116  
# used version during this work: torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
# for older cuda ver:
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

  2. Install Isaac Gym preview 4 release https://developer.nvidia.com/isaac-gym

    unzip files to a folder, then install with pip:

    cd isaacgym/python && pip install -e .

    check it is correctly installed by playing:

    cd examples && python 1080_balls_of_solitude.py

  3. Clone this codebase and install our rsl_rl in the training folder

    pip install -e rsl_rl

  4. Install our legged_gym

    pip install -e legged_gym

    Ensure you have installed the following packages:

    • pip install numpy==1.21 (must < 1.24, >1.20)
    • pip install tensorboard
    • pip install setuptools==59.5.0

  5. Try training.

    can use "--headless" to disable gui, press "v" to pause/resume gui play.

    for go1, in legged_gym/legged_gym,

    # agile policy
python scripts/train.py --task=go1_pos_rough --max_iterations=4000 

# agile policy, lagrangian ver
python scripts/train.py --task=go1_pos_rough_ppo_lagrangian --max_iterations=4000

# recovery policy
python scripts/train.py --task=go1_rec_rough --max_iterations=1000

  6. Play the trained policy

    python scripts/play.py --task=go1_pos_rough
python scripts/play.py --task=go1_rec_rough

  7. Use the testbed, and train/test Reach-Avoid network:

    # try testbed
python scripts/testbed.py --task=go1_pos_rough [--load_run=xxx] --num_envs=1

# train RA (be patient it will take time to converge) 
# make sure you have at least exported one policy by play.py so the exported folder exists
python scripts/testbed.py --task=go1_pos_rough --num_envs=1000 --headless --trainRA

# test RA (only when you have trained one RA)
python scripts/testbed.py --task=go1_pos_rough --num_envs=1 --testRA

# evaluate
python scripts/testbed.py --task=go1_pos_rough --num_envs=1000 --headless [--load_run=xxx] [--testRA]

  8. Sample dataset for ray-prediction network training

    python scripts/camrec.py --task=go1_pos_rough --num_envs=3
    • Tips 1: You can edit the shift value in Line 93 and the log_root in Line 87 to collect different dataset files in parallel (so you can merge them by simply moving the files), and manually change the obstacles in env_cfg.asset.object_files in Line 63.
    • Tips 2: After collecting the data, there's a template code in train_depth_resnet.py to train the ray-prediction network, but using what you like for training CV models is highly encouraged!
    • Tips 3: You may change camera configs of resolution, position, FOV, and depth range in the config file Line 151.

Hardware Deployment

System Overview

<p align="center"> <img src="images/hardware.png" width="80%"/> </p>

3D Print Mounts

Deployment Code Installation

Deployment Setup

Deployment Scripts

  1. roscore: Activate ROS Noetic Envrioment
  2. cd src/abs_src/: Enter the ABS scripts file
  3. python publisher_depthimg_linvel.py: Publish ray prediction results and odometry results for navigation goals
  4. python led_control_ros.py: Control the two LED lights based on RA values
  5. python depth_obstacle_depth_goal_ros.py: Activate the Go1 using the agile policy and the recovery policy

Deployment Controllers

Troubleshooting:

Contact

Issues

You can create an issue if you meet any bugs, except:

Credit

If our work does help you, please consider citing us and the following works:

@inproceedings{AgileButSafe,
  author    = {He, Tairan and Zhang, Chong and Xiao, Wenli and He, Guanqi and Liu, Changliu and Shi, Guanya},
  title     = {Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion},
  booktitle = {Robotics: Science and Systems (RSS)},,
  year      = {2024},
}

We used codes in Legged Gym and RSL RL, based on the paper:

Previsou works that heavily inspired the policy training designs:

Previsou works that heavily inspired the RA value design:

Previsou works that heavily inspired the perception design: