ABS

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

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.

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

• Robot: Unitree Go1 EDU
• Perception: ZED mini Camera
• Onboard Compute: Orin NX (16GB)
• LED: PSEQT LED Lights
• Power Regulator: Pololu 12V, 15A Step-Down Voltage Regulator D24V150F12

3D Print Mounts

• Orin NX mount: STL-PCMount_v2
• ZED mini mount: STL-CameraSeat and STL-ZEDMountv1

Deployment Code Installation

• Unitree Go1 SDK
• ZED SDK
• ROS Noetic
• Pytorch on a Python 3 environment

Deployment Setup

• Low Level Control Mode for Unitree Go1: L2+A -> L2+B -> L1+L2+Start

• Network Configuration for Orin NX:

  • IP: 192.168.123.15
  • Netmask: 255.255.255.0

• Convert the .pt files of agile/recovery policy, RA value to .onnx files using src/abs_src/onnx_model_converter.py

• Modify the path of (.onnx or .pt) models in publisher_depthimg_linvel.py and depth_obstacle_depth_goal_ros.py

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

• B: Emergence stop
• Default: Go1 Running ABS based on goal command
• L2: Turn left
• R2: Turn right
• Down: Back
• Up: Stand
• A: Turn around
• X: Back to initial position

Troubleshooting:

Contact

• Deployment and Ray-Prediction: Tairan He, [email protected]
• Policy Learning in Sim: Chong Zhang, [email protected]
• PPO-Lagrangian Implementation: Wenli Xiao, [email protected]

Issues

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

• If you cannot run the vanilla RSL’s Legged Gym, it is expected that you first go to the vanilla Legged Gym repo for help.
• There can be CUDA-related errors when there are too many parallel environments on certain PC+GPU+driver combination: we cannot solve thiss, you can try to reduce num_envs.
• Our codebase is only for our hardware system showcased above. We are happy to make it serve as a reference for the community, but we won’t tune it for your own robots.

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:

• Rudin, Nikita, et al. “Learning to walk in minutes using massively parallel deep reinforcement learning.” CoRL 2022.

Previsou works that heavily inspired the policy training designs:

• Rudin, Nikita, et al. “Advanced skills by learning locomotion and local navigation end-to-end.” 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022.
• Zhang, Chong, et al. “Learning Agile Locomotion on Risky Terrains.” arXiv preprint arXiv:2311.10484 (2023).
• Zhang, Chong, et al. “Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End.” ICRA 2024.

Previsou works that heavily inspired the RA value design:

• Hsu, Kai-Chieh, et al. “Safety and liveness guarantees through reach-avoid reinforcement learning.” RSS 2021.

Previsou works that heavily inspired the perception design:

• Hoeller, David, et al. “Anymal parkour: Learning agile navigation for quadrupedal robots.” Science Robotics 9.88 (2024): eadi7566.
• Acero, F., K. Yuan, and Z. Li. “Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations.” IEEE Robotics and Automation Letters 7.4 (2022): 8611-8618.