让机器人通过提问黑盒伙伴，更高效地学会与人协作。
Shared Control with Black Box Agents using Oracle Queries
- 用直接提问方式获取合作方的最优动作建议
- 三种启发式策略显著降低学习成本，提升控制精度
- 适合研究人机协同与强化学习交互的学者
共享控制问题涉及机器人学习与人类协作。在学习共享控制策略时，短时通信可大幅减少运行时间并提高系统准确性。本文扩展该问题，引入直接查询合作代理的能力。考虑两类响应：提供最优动作（即使短期看似错误）的完整知识型预言家，以及仅掌握自身部分系统信息的有限知识型预言家。基于此额外信息通道，本文提出三种查询时机选择的启发式方法：基于强化学习、基于效用、基于熵。这些策略旨在降低系统整体学习成本。在两个环境上的实证结果表明，查询机制有助于学习更优控制策略，并揭示了所提启发式方法间的权衡关系。
原文摘要 · Abstract (English)
Shared control problems involve a robot learning to collaborate with a human. When learning a shared control policy, short communication between the agents can often significantly reduce running times and improve the system's accuracy. We extend the shared control problem to include the ability to directly query a cooperating agent. We consider two types of potential responses to a query, namely oracles: one that can provide the learner with the best action they should take, even when that action might be myopically wrong, and one with a bounded knowledge limited to its part of the system. Given this additional information channel, this work further presents three heuristics for choosing when to query: reinforcement learning-based, utility-based, and entropy-based. These heuristics aim to reduce a system's overall learning cost. Empirical results on two environments show the benefits of querying to learn a better control policy and the tradeoffs between the proposed heuristics.
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