让机器人多任务协作时更省力，自动选最合适的提问方式。
Optimal Interactive Learning on the Job via Facility Location Planning
- 用设施选址模型优化多任务交互，自动选择提问类型
- 实验显示人类工作量大幅降低，任务成功率保持高水准
- 适合需要长期人机协同的智能机器人场景
协作机器人需持续适应新任务和用户偏好，又不增加用户负担。现有交互学习方法多限于单任务，难以支持长期多任务协作。本文提出COIL（成本最优交互学习）——一种多任务交互规划框架，通过在技能、偏好、帮助三类提问中策略性选择，最小化人类总投入。当用户偏好已知时，将问题建模为无容量设施选址（UFL）问题，利用现成近似算法实现多项式时间内的有界次优规划。针对偏好不确定的情况，引入一步信念空间规划，仍保持多项式时间性能。仿真与物理实验在操作任务中验证：该框架显著减少人类工作量，同时保证任务成功完成。
原文摘要 · Abstract (English)
Collaborative robots must continually adapt to novel tasks and user preferences without overburdening the user. While prior interactive robot learning methods aim to reduce human effort, they are typically limited to single-task scenarios and are not well-suited for sustained, multi-task collaboration. We propose COIL (Cost-Optimal Interactive Learning) -- a multi-task interaction planner that minimizes human effort across a sequence of tasks by strategically selecting among three query types (skill, preference, and help). When user preferences are known, we formulate COIL as an uncapacitated facility location (UFL) problem, which enables bounded-suboptimal planning in polynomial time using off-the-shelf approximation algorithms. We extend our formulation to handle uncertainty in user preferences by incorporating one-step belief space planning, which uses these approximation algorithms as subroutines to maintain polynomial-time performance. Simulated and physical experiments on manipulation tasks show that our framework significantly reduces the amount of work allocated to the human while maintaining successful task completion.
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