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To ensure both the physical and mental safety of humans during human-robot interaction (HRI), a rich body of literature has been accumulated, and the notion of socially acceptable robot behaviors has arisen. To be specific, it requires the motion of robots not only to be physically collision-free but also to consider and respect the social conventions developed and enforced in the human social contexts. Among these social conventions, personal space, or proxemics, is one of the most commonly considered in the robot behavioral design. Nevertheless, most previous research efforts assumed that robots could generate human-like motions by merely mimicking a human. Rarely are the robot’s behavioral algorithms assessed and verified by human participants. Therefore, to fill the research gap, a Turing-like simulation test, which contains the interaction of two agents (each agent could be a human or a robot) in a shared space was conducted. Participants (33 in total) were asked to identify and label the category of those agents followed by questionnaires. Results revealed that people who had different attitudes and prior expectations of appropriate robot behaviors responded to the algorithm differently, and their identification accuracy varied significantly. In general, by considering personal space in the robot obstacle avoidance algorithm, robots could demonstrate more humanlike motion behaviors which are confirmed by human experiments.

Effects of Human Personal Space on the Robot Obstacle Avoidance Be havior: A Human-in-the-loop Assessment