legged-robots-manipulation

Introduction

legged-robots-manipulation is a loco-manipulation repository for (wheel-)legged robots. The code is built on legged_gym.

Project Page:wheel-legged-loco-manipulation (IROS Oral 2024)

The current repository contains airbot ,go2_arx,b2w_z1,aliengo_z1 and b2w. The current repository is a partial implementation of the paper.The repository is still under construction for various reasons, and will be releasing issac lab versions and vision-based versions in the near future.

To do list

• legged-robots-manipulation for isaacgym
• legged-robots-manipulation for isaaclab
• end2end vision-based loco-manipulation method

airbot

go2_arx

aliengo_Z1

B2W_Z1

b2w

b2w is a locomotion demo for the Unitree b2w robot.

Installation

install isaac_gym,rsl_rl,legged_gym

1. You are supposed to install the isaac_gym,rsl_rl and legged_gym.Please install the above dependencies (Isaac Gym Preview 4, rsl_rl, legged_gym) as described in legged_gym.

install legged-robots-manipulation

cd loco_manipulation_gym
pip install -e .

Train

python tran.py --task=b2w --rl_device=cuda:0

# or

python train.py --task=airbot  --rl_device=cuda:0 

Citing

If you use this work, please cite this paper:

@INPROCEEDINGS{10802062,
  author={Wang, Zifan and Jia, Yufei and Shi, Lu and Wang, Haoyu and Zhao, Haizhou and Li, Xueyang and Zhou, Jinni and Ma, Jun and Zhou, Guyue},
  booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={Arm-Constrained Curriculum Learning for Loco-Manipulation of a Wheel-Legged Robot}, 
  year={2024},
  volume={},
  number={},
  pages={10770-10776},
  doi={10.1109/IROS58592.2024.10802062}}

IEEE concur reimbursement process reference:

IEEE_concur_reimbursement_process.pdf

Acknowledgements

This code builds upon following open-source code-bases. Please visit the URLs to see the respective LICENSES:

1. https://github.com/leggedrobotics/legged_gym/tree/master
2. https://github.com/aCodeDog/awesome-loco-manipulation
3. https://github.com/MarkFzp/Deep-Whole-Body-Control