让机器人根据人类信任状态决定何时求助，提升人机协作效率。
When To Seek Help: Trust-Aware Assistance Seeking in Human-Supervised Autonomy
- 用部分可观测马尔可夫决策过程建模人类信任，指导机器人决策。
- 高复杂度任务中求助可提升人类信任，干预多发生在信任低时。
- 模型预测的信任与真实自评高度一致，适合实际人机系统部署。
本文旨在建模并实验评估人机团队中信任的演变，以预测未来信念与行为。研究表明，维持人机团队间的信任对任务成功至关重要。信任是多维度、隐含的状态，受过往经历与未来行动共同影响。在人机协同任务中，设计基于部分可观测马尔可夫决策过程（POMDP）的最优求助策略。实验中，人类监督者负责确保自主移动机械臂在环境中安全收集物体。机器人可自主尝试采集或主动请求帮助。监督者持续监控，按需提供协助或干预。此处人类信任为隐藏状态，目标是最优团队性能。通过两轮人机交互实验，第一轮数据用于估计POMDP参数，第二轮用于验证策略。结果表明，多数参与者在信任较低时更可能干预，尤其在高复杂度任务中。机器人在高复杂度任务中请求帮助可正向影响人类信任。实验显示，该信任感知策略优于无信信任策略。通过对比仅基于行为数据的模型推断与自评信任值，发现二者具有同构性。
原文摘要 · Abstract (English)
Our goal is to model and experimentally assess trust evolution to predict future beliefs and behaviors of human-robot teams in dynamic environments. Research suggests that maintaining trust among team members in a human-robot team is vital for successful team performance. Research suggests that trust is a multi-dimensional and latent entity that relates to past experiences and future actions in a complex manner. Employing a human-robot collaborative task, we design an optimal assistance-seeking strategy for the robot using a POMDP framework. In the task, the human supervises an autonomous mobile manipulator collecting objects in an environment. The supervisor's task is to ensure that the robot safely executes its task. The robot can either choose to attempt to collect the object or seek human assistance. The human supervisor actively monitors the robot's activities, offering assistance upon request, and intervening if they perceive the robot may fail. In this setting, human trust is the hidden state, and the primary objective is to optimize team performance. We execute two sets of human-robot interaction experiments. The data from the first experiment are used to estimate POMDP parameters, which are used to compute an optimal assistance-seeking policy evaluated in the second experiment. The estimated POMDP parameters reveal that, for most participants, human intervention is more probable when trust is low, particularly in high-complexity tasks. Our estimates suggest that the robot's action of asking for assistance in high-complexity tasks can positively impact human trust. Our experimental results show that the proposed trust-aware policy is better than an optimal trust-agnostic policy. By comparing model estimates of human trust, obtained using only behavioral data, with the collected self-reported trust values, we show that model estimates are isomorphic to self-reported responses.
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