Understanding Preferred Robot Reaction Times for Human-Robot Handovers Supported by a Deep Learning System

Jan Leusmann, Ludwig Felder, Chao Wang, Sven Mayer

Abstract: Human-human handovers are natural and seamless. To be able to do this, humans optimize towards many factors. One of them is the timing when receiving an object. However, the preferred robot reaction time in Human-Robot handovers is currently unclear. To understand the preferred robot reaction time, we trained an Space-Time-Separable Graph Convolutional Network (STS-GCN) model using motion capture data of human-human handovers. We deployed this system on a robotic arm with live depth camera data. We conducted a user study (N=20) with five robot reaction times. We found that users perceived an early prediction as preferred. Furthermore, we found that designers can adapt this timing to their needs based on six sub-components of user perception. We contribute a ready-to-deploy hand over classification model, a preferred handover time for our system, and an approach to determine the preferred robot reaction time for robotic systems.