Enabling Multi-Robot Collaboration from Single-Human Guidance
- URL: http://arxiv.org/abs/2409.19831v1
- Date: Mon, 30 Sep 2024 00:02:56 GMT
- Title: Enabling Multi-Robot Collaboration from Single-Human Guidance
- Authors: Zhengran Ji, Lingyu Zhang, Paul Sajda, Boyuan Chen,
- Abstract summary: We propose an efficient way of learning collaborative behaviors in multi-agent systems by leveraging expertise from only a single human.
We show that agents can effectively learn to collaborate by allowing a human operator to dynamically switch between controlling agents for a short period.
Our experiments showed that our method improves the success rate of a challenging collaborative hide-and-seek task by up to 58$% with only 40 minutes of human guidance.
- Score: 5.016558275355615
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Learning collaborative behaviors is essential for multi-agent systems. Traditionally, multi-agent reinforcement learning solves this implicitly through a joint reward and centralized observations, assuming collaborative behavior will emerge. Other studies propose to learn from demonstrations of a group of collaborative experts. Instead, we propose an efficient and explicit way of learning collaborative behaviors in multi-agent systems by leveraging expertise from only a single human. Our insight is that humans can naturally take on various roles in a team. We show that agents can effectively learn to collaborate by allowing a human operator to dynamically switch between controlling agents for a short period and incorporating a human-like theory-of-mind model of teammates. Our experiments showed that our method improves the success rate of a challenging collaborative hide-and-seek task by up to 58$% 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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