仅用40分钟人类指导，让机器人团队高效协作完成隐藏任务
Enabling Multi-Robot Collaboration from Single-Human Guidance
- 人类动态切换控制不同机器人，并注入对队友的共情理解模型
- 协作成功率提升58%，仅需40分钟人类示范
- 方法可迁移至真实机器人，适合需要少样本协作训练的场景
学习协作行为对多智能体系统至关重要。传统多智能体强化学习通过联合奖励和集中观测隐式求解，假设协作会自然涌现；也有研究基于一组协作专家的示范进行学习。本文提出一种高效且显式的多智能体协作学习方法，仅需单一人类专家的指导。核心思想是：人类在团队中能自然承担多种角色。我们通过让人类操作员短暂切换控制不同智能体，并引入类人心理理论（theory-of-mind）模型模拟队友意图，使智能体有效学习协作。实验表明，在一个具有挑战性的协同躲藏任务中，该方法成功率达58%的提升，仅需40分钟人类指导。进一步通过多机器人真实世界实验验证了方法的可迁移性。
原文摘要 · Abstract (English)
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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