Enabling Multi-Robot Collaboration from Single-Human Guidance
<|reference_start|>Enabling Multi-Robot Collaboration from Single-Human Guidance: 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.<|reference_end|>
@article{ji2024enabling,
    title={Enabling Multi-Robot Collaboration from Single-Human Guidance},
    author={Zhengran Ji, Lingyu Zhang, Paul Sajda, Boyuan Chen},
    journal={arXiv preprint arXiv:2409.19831},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19831},
    primaryClass={cs.RO cs.HC cs.LG cs.MA}
}