Active Inference-Based Planning for Safe Human-Robot Interaction: Concurrent Consideration of Human Characteristic and Rationality

This letter proposes a motion planning strategy for a robot to safely interact with humans exhibiting uncertain actions. The human actions are often encoded by the internal states that are attributed to human characteristics and rationality. First, by leveraging a continuous level of rationality, we compute the belief on human rationality along with his/her characteristic. This systematically reasons out the uncertainty in the observed human action, thereby better assessing the potential safety risks during the interaction. Second, based on the computed belief over the human internal states, we formulate a Stochastic Model Predictive Control (SMPC) problem to plan the robot's actions such that it safely achieves its goal while also actively inferring on the human internal state. To cope with the expensive computation of the SMPC, we develop a sampling-based technique that efficiently evaluates the robot's action conditioned on human uncertainty. The experiment results demonstrate that the proposed strategy excels in human action prediction, and significantly improves the safety and efficiency of Human-Robot Interaction (HRI).