标题： 通过设施位置规划实现工作中的最优交互学习 显示英文标题

标题： Optimal Interactive Learning on the Job via Facility Location Planning

摘要： 协作机器人必须不断适应新任务和用户偏好，而不会给用户带来过重的负担。虽然先前的交互式机器人学习方法旨在减少人力投入，但它们通常仅限于单一任务场景，并不适合持续的多任务协作。我们提出了COIL（成本最优交互学习）——一个多任务交互规划器，通过战略性地选择三种查询类型（技能、偏好和帮助），在一系列任务中最小化人力投入。当用户偏好已知时，我们将COIL形式化为一个未容量限制设施选址（UFL）问题，这使得使用现成的近似算法可以在多项式时间内进行有界次优规划。我们通过引入一步信念空间规划扩展了我们的公式，以处理用户偏好的不确定性，这些近似算法作为子程序使用以保持多项式时间性能。仿真和物理实验表明，我们的框架显著减少了分配给人类的工作量，同时保持了任务成功完成。 显示英文摘要

摘要： 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.