Isaac Lab Kangaroo

This project contains Isaac Lab locomotion tasks for the Kangaroo robot from PAL Robotics (1st version). The repository contains both the environment definitions and the USD models of the robot. It allows you to develop in an isolated environment, outside of the core Isaac Lab repository. (Built using Isaac Lab External Project Template)

Available Tasks:

• Kangaroo-Locomotion-Flat-v0 Locomotion on flat terrains (tested).
• Kangaroo-Locomotion-Rough-v0 Locomotion on rough terrains (not tested yet).

Installation

• Install Isaac Lab by following the installation guide. We recommend using the conda/venv installation as it simplifies calling Python scripts from the terminal.

• Clone or copy this repository separately from the Isaac Lab installation (i.e. outside the IsaacLab directory):

• Using a python interpreter that has Isaac Lab installed (use FULL_PATH_TO_isaaclab.sh|bat -p instead of python if Isaac Lab is not installed in Python venv or conda), install the library in editable mode using:

  python -m pip install -e source/isaaclab_kangaroo

• Verify that the extension is correctly installed by:

  • Listing the available tasks:

    python scripts/list_envs.py

  • Spawning a Kangaroo in Isaac Sim:

    python scripts/<RL_LIBRARY>/spawn_kangaroo.py

  • Running a task with dummy agents:

    • Zero-action agent

      python scripts/zero_agent.py --task=<TASK_NAME>

    • Random-action agent

      python scripts/random_agent.py --task=<TASK_NAME>

Set up IDE (Optional)

To setup the IDE, please follow these instructions:

• Run VSCode Tasks, by pressing Ctrl+Shift+P, selecting Tasks: Run Task and running the setup_python_env in the drop down menu. When running this task, you will be prompted to add the absolute path to your Isaac Sim installation.

If everything executes correctly, it should create a file .python.env in the .vscode directory. The file contains the python paths to all the extensions provided by Isaac Sim and Omniverse. This helps in indexing all the python modules for intelligent suggestions while writing code.

Example Usage

• Running a training in headless mode (this will create a logs folder inside the main repository to log training results).

  python scripts/train.py --task=Kangaroo-Locomotion-Flat-v0 --headless

• Playing the trained policy while recording a video (inside the logs folder).

  python scripts/play.py --task=Kangaroo-Locomotion-Flat-v0 --num_envs=8 --video --headless

Code formatting

We have a pre-commit template to automatically format your code. To install pre-commit:

pip install pre-commit

Then you can run pre-commit with:

pre-commit run --all-files

BibTeX

@article{amadio2025learning,
  title={Learning to Walk with Hybrid Serial-Parallel Linkages: a Case Study on the Kangaroo Robot},
  author={Amadio, Fabio and Li, Hongbo and Uttini, Lorenzo and Ivaldi, Serena and Modugno, Valerio and Hoffman, Enrico Mingo},
  year={2025}
}