Integrating Theory of Mind into Embodied Agents for Human-Aware Interaction

As embodied artificial agents (social robots, virtual assistants, and collaborative manipulators) move from controlled laboratories into homes, hospitals, and workplaces, their ability to interpret and anticipate human mental states becomes as important as their physical competence. This paper presents a unified theoretical and architectural account of how Theory of Mind (ToM), the capacity to attribute beliefs, desires, intentions, and emotions to others, can be integrated into embodied agents to support human-aware interaction. We first formalize a computational notion of ToM suitable for embodied settings, distinguishing zero-order perceptual inference from first- and second-order recursive belief attribution, and we relate these levels to the demands of everyday human-agent collaboration. We then propose a modular architecture, the Belief-Intention-Action (BIA) framework, that couples multimodal perception with a hybrid Bayesian-neural mental-state estimator, a hierarchical intention inference module, and an action-selection policy that explicitly conditions on inferred human mental states. The architecture is instantiated on both a simulated humanoid platform and a physical mobile manipulator and evaluated across three interaction scenarios: collaborative object handover, ambiguous instruction resolution, and false-belief-sensitive assistance. Compared with baseline agents lacking explicit mental-state modeling, the ToM-equipped agent achieves higher task success, faster adaptation to human error, and improved subjective ratings of perceived understanding and comfort, while ablation studies confirm the contribution of second-order reasoning to false-belief tasks. We conclude by discussing implications for trust calibration, the computational cost of recursive mental-state reasoning, and the ethical responsibilities that accompany machines that model human minds.