Robots operating in unstructured human environments inevitably encounter failures, especially in robot caregiving scenarios. While humans can often help robots recover, excessive or poorly targeted queries impose unnecessary cognitive and physical workload on the human partner. We present a human-in-the-loop failure-recovery framework for modular robotic policies, where a policy is composed of distinct modules such as perception, planning, and control, any of which may fail and often require different forms of human feedback. Our framework integrates calibrated estimates of module-level uncertainty with models of human intervention cost to decide which module to query and when to query the human. It separates these two decisions: a module selector identifies the module most likely responsible for failure, and a querying algorithm determines whether to solicit human input or act autonomously. We evaluate several module-selection strategies and querying algorithms in controlled synthetic experiments, revealing trade-offs between recovery efficiency, robustness to system and user variables, and user workload. Finally, we deploy the framework on a robot-assisted bite acquisition system and demonstrate, in studies involving individuals with both emulated and real mobility limitations, that it improves recovery success while reducing the workload imposed on users. Our results highlight how explicitly reasoning about both robot uncertainty and human effort can enable more efficient and user-centered failure recovery in collaborative robots. Supplementary materials and videos can be found at: http://emprise.cs.cornell.edu/modularhil

翻译：机器人在非结构化人类环境中运行时不可避免地会遇到故障，这在机器人护理场景中尤为常见。虽然人类通常能协助机器人恢复，但过多或针对性不足的查询会给人类伙伴带来不必要的认知与身体负担。本文提出一种面向模块化机器人策略的人机协同故障恢复框架：该策略由感知、规划、控制等独立模块构成，任一模块均可能失效且往往需要不同形式的人类反馈。本框架通过整合模块级不确定性校准估计与人工干预成本模型，以决策应查询哪个模块及何时向人类发起查询。该框架将两项决策分离：模块选择器识别最可能导致故障的模块，查询算法则决定是请求人工输入还是自主行动。我们在受控合成实验中评估了多种模块选择策略与查询算法，揭示了恢复效率、系统及用户变量鲁棒性以及用户负担三者间的权衡关系。最后，我们将该框架部署于机器人辅助进食采集系统，并在模拟及真实行动受限受试者的实验中证明：该框架能提升恢复成功率，同时降低用户负担。研究结果凸显了通过显式推理机器人不确定性与人类操作成本，能够为协作机器人实现更高效且以用户为中心的故障恢复。补充材料与视频详见：http://emprise.cs.cornell.edu/modularhil