让机器人具备心理理论能力，能更好理解用户意图并提升协作效果。
Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of Mind
- 分两层架构：上层用Q-learning学行为策略，下层用启发式方法推断用户意图。
- 56名用户实测显示，有心理理论的机器人使用户表现更优、接受率更高。
- 适合研究人机协作、具身智能与社会机器人方向的学者参考。
适应用户偏好及推断和解读人类信念与意图（即心理理论，ToM）是实现高效人机协作的关键。尽管重要性突出，但具备ToM能力的自适应机器人研究仍很少。本文开展一项探索性对比研究，考察配备ToM能力的社会机器人对用户表现与感知的影响。设计了双层架构：第一层为基于Q-learning的智能体，学习机器人的高层行为；第二层为基于启发式的ToM模块，用于推断用户意图策略，并负责执行协助行为及解释决策动机。在真实场景中招募56名参与者，分别与具备或不具ToM能力的机器人交互。结果表明，处于ToM条件下的用户表现更佳，更频繁接受机器人帮助，且认为其具备更强的适应性、预测力与意图识别能力。初步发现可为未来复杂自适应行为计算架构的设计提供参考。
原文摘要 · Abstract (English)
The adaptation to users' preferences and the ability to infer and interpret humans' beliefs and intents, which is known as the Theory of Mind (ToM), are two crucial aspects for achieving effective human-robot collaboration. Despite its importance, very few studies have investigated the impact of adaptive robots with ToM abilities. In this work, we present an exploratory comparative study to investigate how social robots equipped with ToM abilities impact users' performance and perception. We design a two-layer architecture. The Q-learning agent on the first layer learns the robot's higher-level behaviour. On the second layer, a heuristic-based ToM infers the user's intended strategy and is responsible for implementing the robot's assistance, as well as providing the motivation behind its choice. We conducted a user study in a real-world setting, involving 56 participants who interacted with either an adaptive robot capable of ToM, or with a robot lacking such abilities. Our findings suggest that participants in the ToM condition performed better, accepted the robot's assistance more often, and perceived its ability to adapt, predict and recognise their intents to a higher degree. Our preliminary insights could inform future research and pave the way for designing more complex computation architectures for adaptive behaviour with ToM capabilities.
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