• 🔥 Highlights
• 🛠️ Installation
• 📷 Data Collection
• 🤖 Policy Training
• 🌐 Multi-Robot Deployment
• 📝 TODO
• ✍️ Citation
• 🪪 License
• 🙏 Acknowledgement

• Our failure detector, FLOAT, achieves nearly 95% accuracy in four real-world tasks, improving the SOTA online failure detection approaches by over 20%.
• ARMADA leads to a more than 4× increase in success rate and a greater than 2× decrease in human intervention ratio compared to previous human-in-the-loop learning approaches that require full-time human supervision.
• ARMADA conduces to saliently larger improvement in task progress and data efficiency using more robots in parallel, and expedites policy adaptation to novel scenarios.

We test our codebase on Python 3.10. Please create an environment named armada using the following command.

conda env create -f conda_environment.yaml

If you'd like to test on real robot, execute the following command.

conda env create -f conda_environment_real.yaml

Please refer to hardware setup guide for more information.

The following example collects expert demonstrations with an image resolution of 224*224 and a 10Hz control frequency. Please feel free to tailor it to your own needs.

python record.py --output /path/to/your/output/path --resolution 224 224 --fps 10

The main training code can be found in armada/diffusion_policy, and the corresponding configuration files are placed under armada/config/training.

An example usage of our training recipe is in armada/train.sh.

Please refer to Multi-robot deployment guide for more information.

• Release the training code and one-to-multiple shared control codebase.
• Release the code for multiple-to-multiple control.

@misc{yu2025armadaautonomousonlinefailure,
      title={ARMADA: Autonomous Online Failure Detection and Human Shared Control Empower Scalable Real-world Deployment and Adaptation}, 
      author={Wenye Yu and Jun Lv and Zixi Ying and Yang Jin and Chuan Wen and Cewu Lu},
      year={2025},
      eprint={2510.02298},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2510.02298}, 
}

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International Public License.

Our code is built upon Diffusion Policy. Thanks for their open-source effort!