用控制理论建模人类心理状态，让机器人更懂你、更持久地互动。
Leveraging Systems and Control Theory for Social Robotics: A Model-Based Behavioral Control Approach to Human-Robot Interaction
- 通过动态模型捕捉用户信念、目标和情绪变化，实现行为自适应。
- 实验中对心理状态追踪误差仅0.067，比无模型方法提升16%参与度。
- 适合关注人机交互个性化与长期陪伴的机器人研发者。
社交机器人（SRs）应能自主与人类互动，并表现出与其角色相符的社会行为。在医疗、教育和陪伴领域，它们有望提升生活质量。然而，由于对人类心理状态理解有限，个性化和持续吸引用户仍是挑战。为此，本文引入一种近期提出的数学动态模型，用于描述人类感知、认知与决策过程。通过识别该模型参数并部署于机器人行为控制系统中，可有效根据用户心理状态演化进行个性化响应，从而增强长期参与度与个性化体验。本方法通过建模不可见的心理状态动态，实现了机器人自主适应性，显著提升了透明性与意识水平。我们在10名参与者与Nao机器人进行三轮国际象棋谜题互动实验中验证了该系统，每轮45-90分钟。模型在追踪用户信念、目标和情绪方面达到均方误差0.067（占最大可能误差的1.675%）。相比不追踪心理状态的模型无关控制器，本方法平均提升参与度16%。事后问卷反馈进一步确认了模型驱动机器人在感知与互动上的高可信度。结果表明，基于模型的方法与控制理论在推进人机交互方面具有独特潜力。
原文摘要 · Abstract (English)
Social robots (SRs) should autonomously interact with humans, while exhibiting proper social behaviors associated to their role. By contributing to health-care, education, and companionship, SRs will enhance life quality. However, personalization and sustaining user engagement remain a challenge for SRs, due to their limited understanding of human mental states. Accordingly, we leverage a recently introduced mathematical dynamic model of human perception, cognition, and decision-making for SRs. Identifying the parameters of this model and deploying it in behavioral steering system of SRs allows to effectively personalize the responses of SRs to evolving mental states of their users, enhancing long-term engagement and personalization. Our approach uniquely enables autonomous adaptability of SRs by modeling the dynamics of invisible mental states, significantly contributing to the transparency and awareness of SRs. We validated our model-based control system in experiments with 10 participants who interacted with a Nao robot over three chess puzzle sessions, 45 - 90 minutes each. The identified model achieved a mean squared error (MSE) of 0.067 (i.e., 1.675% of the maximum possible MSE) in tracking beliefs, goals, and emotions of participants. Compared to a model-free controller that did not track mental states of participants, our approach increased engagement by 16% on average. Post-interaction feedback of participants (provided via dedicated questionnaires) further confirmed the perceived engagement and awareness of the model-driven robot. These results highlight the unique potential of model-based approaches and control theory in advancing human-SR interactions.
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