AToM: 長期的な人間-ロボットインタラクションにおける適応的な心の理論に基づく人間動作予測
AToM: Adaptive Theory-of-Mind-Based Human Motion Prediction in Long-Term Human-Robot Interactions
心の理論に基づき、ゲーム理論モデルとアンセンテッドカルマンフィルタを用いて人間の内部信念を推定し、長期的な人間-ロボットインタラクションにおける人間の動作を適応的に予測する手法を提案。
著者: Yuwen Liao, Muqing Cao, Xinhang Xu, Lihua Xie
分類: cs.RO
原文アブストラクト
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.
