Learning Human–Robot Proxemics Models from Experimental Data

Humans in a society generally tend to implicitly adhere to the shared social norms established within that culture. Robots operating in a dynamic environment shared with humans are also expected to behave socially to improve their interaction and enhance their likability among humans. Especially when moving into close proximity of their human partners, robots should convey perceived safety and intelligence. In this work, we model human proxemics as robot navigation costs, allowing the robot to exhibit avoidance behavior around humans or to initiate interactions when engagement is required. The proxemic model enhances robot navigation by incorporating human-aware behaviors, treating humans not as mere obstacles but as social agents with personal space preferences. The model of interaction positions estimates suitable locations relative to the target person for the robot to approach when an engagement occurs. Our evaluation on human–robot interaction data and simulation experiments demonstrates the effectiveness of the proposed models in guiding the robot’s avoidance and approaching behaviors toward humans.