Overview

Learning collaborative behaviors is essential for multi-agent systems. Traditionally, multi-agent reinforcement learning solves this implicitly through a joint reward and centralized observations, assuming collaborative behavior will emerge. Other studies propose to learn from demonstrations of a group of collaborative experts. Instead, we propose an efficient and explicit way of learning collaborative behaviors in multi-agent systems by leveraging expertise from only a single human. Our insight is that humans can naturally take on various roles in a team. We show that agents can effectively learn to collaborate by allowing a human operator to dynamically switch between controlling agents for a short period and incorporating a human-like theory-of-mind model of teammates. Our experiments showed that our method improves the success rate of a challenging collaborative hide-and-seek task by up to 58% with only 40 minutes of human guidance. We further demonstrate our findings transfer to the real world by conducting multi-robot experiments.

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Paper

Check out our paper linked here.

Codebase

Check out our codebase at https://github.com/generalroboticslab/HUMAC

Citation

@misc{ji2024enablingmultirobotcollaborationsinglehuman,
      title={Enabling Multi-Robot Collaboration from Single-Human Guidance}, 
      author={Zhengran Ji and Lingyu Zhang and Paul Sajda and Boyuan Chen},
      year={2024},
      eprint={2409.19831},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2409.19831}, 
}     

Acknowledgment

This work is supported in part by ARL STRONG program under awards W911NF2320182 and W911NF2220113.

Contact

If you have any questions, please feel free to contact Zhengran Ji.