Maximising Coefficiency of Human-Robot Handovers through Reinforcement Learning

Collaborative robots need to possess the ability to hand objects properly to humans. Earlier studies on robot-to human handovers have centred around enhancing the human partner’s performance and reducing the physical exertion required to grip the object. Nonetheless, robots exhibiting overly altruistic behaviours may generate protracted and awkward movements that create uncomfortable feelings for humans and affect perceived safety and social acceptance. This paper examines whether applying the cognitive science principle that “humans act coefficiently as a group” in human-robot collaboration - i.e. maximising the benefits for all parties involved simultaneously - leads to a smoother and more natural interaction. Human-robot coefficiency is modelled by online monitoring of human comfort and discomfort indicators and computing robot energy consumption. This score is used by a reinforcement learning problem to adaptively learn the optimal combination of robot interaction parameters to maximise such coefficiency during the task execution. Results demonstrated that by acting coefficiently, the robot accommodated the individual preferences of the majority of participants and enhanced the human perceived comfort.