Enabling Multi-Robot Collaboration from Single-Human Guidance
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916630069510144 |
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| author | Ji, Zhengran Zhang, Lingyu Sajda, Paul Chen, Boyuan |
| author_facet | Ji, Zhengran Zhang, Lingyu Sajda, Paul Chen, Boyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19831 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enabling Multi-Robot Collaboration from Single-Human Guidance Ji, Zhengran Zhang, Lingyu Sajda, Paul Chen, Boyuan Robotics Human-Computer Interaction Machine Learning Multiagent Systems 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. |
| title | Enabling Multi-Robot Collaboration from Single-Human Guidance |
| topic | Robotics Human-Computer Interaction Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2409.19831 |