Gaussian Process-Based Prediction of Human Trajectories to Promote Seamless Human-Robot Handovers

Humans can perform seamless object handovers with little to no effort. These handovers are characterized by an early movement onset that anticipates the handover location and a smooth velocity profile with minimal trajectory corrections. Replicating these characteristics in an object handover task between humans and robots presents a significant modeling challenge. In this paper we implement a Gaussian Process prediction model to serve as a robotic surrogate of human inference, and investigate how this model affects the kinematics of a human giver handing an object to the robot. Additionally, we analyze how the resulting robot kinematics compare to those of a human, and gauge human comfort through subjective reporting. Human giver kinematics during human-robot handover compared closely to human-human giver kinematics with respect to movement speed, movement timing, movement smoothness, and handover distance. Notable differences were observed in reach time and receiver peak transport velocity. When asked how well four attributes of their human-robot handovers (receiver competence, handover comfort, handover naturalness, handover safety) compared to those attributes in human-human handovers, subjects gave mean scores ranging from 4.43 (naturalness) to 5.13 (safety) on a 7 point Likert scale.