/SymmLoco

Primary LanguagePython

Leveraging Symmetry in RL-based Legged Locomotion Control

Table of contents

  1. Overview
  2. Installation
  3. Training a Model
  4. Playing a trained policy
  5. Bibtex

Overview

This repository provides an implementation of the paper:

Leveraging Symmetry in RL-based Legged Locomotion Control
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
paper / project page

This repository provides the environment used to train cyberdog2 to perform four tasks: Door Pushing, Stand Turning (Stand Dancing), Slope Walking and Dribbling. This branch implements Door Pushing, Stand Turning and Slope Walking, while the branch dribbling implements the Dribbling task. The training process uses three different methods: the vanilla PPO (mlp), PPO with data augmentation (aug), PPO with equivariant / invariant networks (emlp).

The code is modified from Isaac Gym Environments for Legged Robots and based on legged_stand_dance and MorphoSymm.

Installation

Create a new conda environment with Python (3.8 suggested)

conda create -n symmloco python==3.8
conda activate symmloco

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

  1. Download and install Isaac Gym Preview 4 from https://developer.nvidia.com/isaac-gym

  2. unzip the file via:

    tar -xf IsaacGym_Preview_4_Package.tar.gz

  3. now install the python package

    cd isaacgym/python && pip install -e .

  4. Verify the installation by try running an example

    python examples/1080_balls_of_solitude.py

  5. For troubleshooting check docs isaacgym/docs/index.html

Install the MorphoSymm, rsl_rl and legged_gym packages

In SymmLoco/MorphoSymm, run pip install -e .

In SymmLoco/rsl_rl, run pip install -e .

In SymmLoco/legged_gym, run pip install -e .

Install other dependencies

pip install tensorboard wandb

Training a model

In SymmLoco/legged_gym, run

python legged_gym/scripts/train.py --task=cyber2_push_door_emlp --headless --right
  • To run on CPU add following arguments: --sim_device=cpu, --rl_device=cpu (sim on CPU and rl on GPU is possible).
  • To run headless (no rendering) use --headless.
  • Important: To improve performance, once the training starts press v to stop the rendering. You can then enable it later to check the progress.
  • The trained policy is saved in legged_gym/logs/<experiment_name>/<date_time>_<run_name>/model_<iteration>.pt. Where <experiment_name> and <run_name> are defined in the train config.
  • The following command line arguments override the values set in the config files:
    • --task TASK: Task name. All supported tasks can be found in legged_gym/scripts/train.py.
    • --resume: Resume training from a checkpoint
    • --experiment_name EXPERIMENT_NAME: Name of the experiment to run or load.
    • --run_name RUN_NAME: Name of the run.
    • --load_run LOAD_RUN: Name of the run to load when resume=True. If -1: will load the last run.
    • --checkpoint CHECKPOINT: Saved model checkpoint number. If -1: will load the last checkpoint.
    • --num_envs NUM_ENVS: Number of environments to create.
    • --seed SEED: Random seed.
    • --max_iterations MAX_ITERATIONS: Maximum number of training iterations.
    • --left: Set the door open direction to left. If not set, half number of doors are set to left and half are set to right.
    • --right: Set the door open direction to right. If not set, half number of doors are set to left and half are set to right.

Troubleshooting

  1. If you get the following error: ImportError: libpython3.8m.so.1.0: cannot open shared object file: No such file or directory, do: sudo apt install libpython3.8

Playing a trained policy

We provide a checkpoint of Door Pushing task trained by emlp. Play the trained policy by running:

python legged_gym/scripts/play.py --task=cyber2_push_door_emlp --load_run=2024-09-17-23-10-31_ --checkpoint=20000

You should see a quadrupedal robot standing up on its rear legs and pushing the door open.

Bibtex 

@inproceedings{su2024leveraging,
    title={Leveraging Symmetry in RL-based Legged Locomotion Control},
    author={Su, Zhi and Huang, Xiaoyu and Ordoñez-Apraez, Daniel and Li, Yunfei and Li, Zhongyu and Liao, Qiayuan and Turrisi, Giulio and Pontil, Massimiliano and Semini, Claudio and Wu, Yi and Sreenath, Koushil},
    booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    year={2024},
    organization={IEEE}
}