RAMBO: RL-augmented Model-based Whole-body Control for Loco-manipulation

Jin Cheng¹, Dongho Kang¹, Gabriele Fadini¹, Guanya Shi², Stelian Coros¹

¹ ETH Zurich, ² Carnegie Mellon University

Accepted to IEEE Robotics and Automation Letters (RA-L) 2025.

Paper | Arxiv | Video | Website

Installation

1. Install isaacsim in a conda environment

   conda create -n rambo python=3.10
   conda activate rambo
   pip install --upgrade pip
   pip install 'isaacsim[all,extscache]==4.5.0' --extra-index-url https://pypi.nvidia.com
   

2. Install isaaclab and RAMBO source. We use our in-house learning framework based on rsl-rl, which we call crl2.

   pip install -e source/isaaclab
   pip install -e source/isaaclab_assets
   pip install -e source/isaaclab_mimic
   pip install -e source/isaaclab_rl
   pip install -e source/isaaclab_tasks
   pip install -e source/crl2
   

3. Install other dependencies

   pip install wandb
   pip install qpth

Train & Play

To make wandb work, do wandb login first in conda environment. And export WANDB_USERNAME in your shell.

Due to memory constraints, wandb will upload the videos after the training is done. You can still view the training videos locally in the log folder.

To train:

# in the root directory of IsaacLab
python scripts/reinforcement_learning/crl2/train.py --task Isaac-RAMBO-Quadruped-Go2-v0 --headless --logger=wandb --video
#python scripts/reinforcement_learning/crl2/train.py --task Isaac-RAMBO-Biped-Go2-v0 --headless --logger=wandb --video

To play:

# in the root directory of IsaacLab
python scripts/reinforcement_learning/crl2/play.py --task Isaac-RAMBO-Quadruped-Go2-v0 --num_envs 10
#python scripts/reinforcement_learning/crl2/play.py --task Isaac-RAMBO-Biped-Go2-v0 --num_envs 10

Citation

If you use this code in your research, please cite our paper:

@article{cheng2025rambo,
  title={RAMBO: RL-augmented Model-based Optimal Control for Whole-body Loco-manipulation},
  author={Cheng, Jin and Kang, Dongho and Fadini, Gabriele and Shi, Guanya and Coros, Stelian},
  journal={arXiv preprint arXiv:2504.06662},
  year={2025}
}

License

This codebase is under CC BY-NC 4.0 license. You may not use the material for commercial purposes, e.g., to make demos to advertise your commercial products.

Acknowledgements

• CAJun: Our QP based optimization module is inspired by the cajun project.
• RSL_RL: Our RL framework crl2 is based on rsl_rl library for the PPO implementation.
• IsaacLab: We use the isaaclab library for the RL training and evaluation.

Contact

Feel free to open an issue or discussion if you encounter any problems or have questions about this project.

For collaborations, feedback, or further inquiries, please reach out to:

• Jin Cheng: jin.cheng@inf.ethz.ch.

We welcome contributions and are happy to support the community in building upon this work!