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.

翻译：适应使用者的偏好，以及推断并理解人类信念与意图（即心智理论，Theory of Mind，ToM）的能力，是实现有效人机协作的两个关键方面。尽管其重要性不言而喻，但很少有研究探讨具备ToM能力的自适应机器人所产生的影响。在本工作中，我们开展了一项探索性比较研究，旨在探究配备ToM能力的社交机器人如何影响用户的绩效与感知。我们设计了一个双层架构：第一层的Q-learning智能体学习机器人的高层行为；第二层则是一个基于启发式的心智理论模块，用于推断用户的意图策略，负责实施机器人的辅助行为，并提供其选择背后的动机。我们在真实环境中开展了一项用户研究，共有56名参与者参与，他们分别与具备ToM能力的自适应机器人或缺乏此能力的机器人进行交互。我们的研究结果表明，处于ToM条件下的参与者表现更优，更频繁地接受机器人的辅助，并且对其适应、预测和识别自身意图的能力给予了更高程度的评价。我们的初步见解可为未来研究提供参考，并为设计具备ToM能力的、更复杂的自适应行为计算架构铺平道路。