AToM: Adaptive Theory-of-Mind-Based Human Motion Prediction in Long-Term Human-Robot Interactions

TL;DR

This paper introduces AToM, an adaptive Theory-of-Mind-based model for long-term human motion prediction in human-robot interactions, enhancing safety and efficiency through interpretable, belief-based predictions.

Contribution

The paper presents a novel ToM-based prediction model that uses a game-theoretic approach and Kalman filtering to adaptively infer human beliefs and predict motions in long-term interactions.

Findings

Effective long-term human motion prediction demonstrated in simulations.

Improved robot safety and efficiency in real-world experiments.

Provides interpretable insights into human beliefs and intentions.

Abstract

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.