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Learning Diverse Skills for Local Navigation under Multi-constraint Optimality (ICRA-2024)

Website | Paper | Supplementary Video

Authors: Jin Cheng, Marin Vlastelica, Pavel Kolev, Chenhao Li, Georg Martius

Autonomous Learning Group, Max Planck Institute for Intelligent Systems, Tübingen, Germany

ETH Zürich, Zürich, Switzerland

University of Tübingen, Tübingen, Germany

Installation

  1. Clone the repo:

    git clone git@gitlab.is.tue.mpg.de:autonomous-learning/solo_legged_gym.git

  2. Install poetry

    curl -sSL https://install.python-poetry.org | python3 - --version 1.4.0
echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

  3. Check if this worked so far. Call

    poetry --version

    It should be:

    Poetry (version 1.4.0)

  4. Now we want to use Python 3.8 to create virtual environment. This is ensured to work with isaacgym preview version 4. First make sure that you are in a clean environment. Deactivate all conda env virtualenv environments and delete any .venv in the root folder of your legged_gym clone. Call:

    poetry env use /usr/bin/python3.8

    If get stuck in creating venv in endless loop, try

    export XDG_DATA_HOME=/tmp

  5. Install dependencies

    poetry install

  6. Download IsaacGym from here and extract it to somewhere you like :).

    poetry shell  # enter the virtual environment

    enter the directory of IsaacGym_Preview_4_Package/isaacgym/python, and run

    pip install -e .

    Don't panic if some packages are removed and reinstalled :)

  7. (Optional) Login Weights and Biases

    echo 'export WANDB_USERNAME=<wandb_username>' >> ~/.bashrc
source ~/.bashrc 
poetry run wandb login

    you will be asked to paste the API keys, you can get it from your personal profile.

Troubleshooting

To fix the following error:

ImportError: libpython3.8m.so.1.0: cannot open shared object file: No such file or directory

install python-dev:

sudo apt install libpython3.8

Run the code

Run the scripts from the repository root directory (where .venv is).

You can either choose to enter the environment by runing poetry shell or run the scripts directly by poetry run python <script_name>.

  1. use scripts/train.py to start training. For example:

    python solo_legged_gym/scripts/train.py --task=solo12_dominic_position --w

    When viewer is enabled (which should be the case by default), use v to pause/resume rendering; use b to zoom in the first env/ zoom out.

    In principle, you can specify --w to enable Weights&Biases and specify --dv to disable viewer.

    Special Note on video recording functionality with Wandb:

    • Using video recording may slow down the training process, but for visualization purpose, it is recommended to enable it.
    • To make sure video recording works properly, please enable wandb by '--w' and DON'T use --dv to disable viewer.

  2. use scripts/position_play.py to play the trained skill. For example:

    python solo_legged_gym/scripts/position_play.py --task=solo12_dominic_position

    specify the log data in position_play.py, use r to restart, use number to specify skill.

  3. use scripts/position_play2.py to play all trained skills. For example:

    python solo_legged_gym/scripts/position_play2.py --task=solo12_dominic_position

    specify the log data in position_play2.py, use r to restart, use number to specify skill.

  4. If wandb is not used, tensorboard will be the default writer. For example:

    run tensorboard --logdir logs/solo12_dominic_position

Addtionally

This project is based on legged_gym and rsl_rl. We thank all the contributors for their great work.