Biomimetic Cues Enable Predictive Mechanisms in Simulatedand Physical Robot-Human Object Handovers

Abstract

Object handovers - while representing one of the simplest forms of physical interaction between two agents - involve a complex interplay of predictive and reactive control mechanisms in both agents. As human-human pairs have unrivaled skills in physical collaboration tasks, we take the approach of understanding and applying biomimetic concepts to human-robot interaction. Here, we apply the concept of passer movement cues, that is, slower movement for heavy objects and faster movements for lighter objects, to robot-human handovers. We first show that when a simulated passing agents movement is scaled with object mass, participants as receivers adapt their anticipatory grip forces according to mass in a virtual environment. We then apply the same concept to a physical robot-human handover and show that our approach generalizes to the real-world. The predictive scaling of grip forces is learned iteratively upon repeated presentations of trajectory-mass pairings, whether the masses are presented in a random or blocked order. Overall we demonstrate that the presentation of robotic kinematic cues can provide intuitive and naturalistic human predictive control in object handover. This extends the use of non-verbal cues in robot-human handover tasks and facilitates more legible and efficient physical robot-human interactions.