用心理理论建模人类行为，提升机器人长期交互中的预测精度。
AToM: Adaptive Theory-of-Mind-Based Human Motion Prediction in Long-Term Human-Robot Interactions
- 基于心理理论构建人类信念模型，动态推断其对机器人的预期。
- 通过无迹卡尔曼滤波实时更新行为参数，实现长期交互中的自适应预测。
- 在仿真与真实场景中验证，显著提升机器人规划的安全性与效率。
人类通过观察和经验不断调整行为以优化表现。与这类动态人类交互极具挑战性，因机器人需准确预测其行为以确保安全高效运行。现有研究较少关注长期动态人类交互。本文提出一种基于心理理论（ToM）的自适应人类行为预测模型，该理论是人类理解他人意图与行为的基本社交认知能力。我们采用博弈论模型表征人类对其他参与者的内在信念，以预测导航场景中所有智能体的未来运动。为估计不断变化的信念，引入无迹卡尔曼滤波（UKF）来更新人类内部模型中的行为参数。该方法通过推断人类如何预测机器人，赋予动态行为独特可解释性。在仿真与真实环境中的长期实验表明，该预测方法有效提升下游机器人规划的安全性与效率。代码将公开于 https://github.com/centiLinda/AToM-human-prediction.git。
原文摘要 · Abstract (English)
Humans learn from observations and experiences to adjust their behaviours towards better performance. Interacting with such dynamic humans is challenging, as the robot needs to predict the humans accurately for safe and efficient operations. Long-term interactions with dynamic humans have not been extensively studied by prior works. We propose an adaptive human prediction model based on the Theory-of-Mind (ToM), a fundamental social-cognitive ability that enables humans to infer others' behaviours and intentions. We formulate the human internal belief about others using a game-theoretic model, which predicts the future motions of all agents in a navigation scenario. To estimate an evolving belief, we use an Unscented Kalman Filter to update the behavioural parameters in the human internal model. Our formulation provides unique interpretability to dynamic human behaviours by inferring how the human predicts the robot. We demonstrate through long-term experiments in both simulations and real-world settings that our prediction effectively promotes safety and efficiency in downstream robot planning. Code will be available at https://github.com/centiLinda/AToM-human-prediction.git.
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