Safety and Naturalness Perceptions of Robot-to-Human Handovers Performed by Data-Driven Robotic Mimicry of Human Givers

Abstract

We study human perceptions of a robot that performs robot-to-human (R2H) handovers controlled to grasp, transport, and transfer 34 objects by mimicking human givers in human-human (H2H) handover data. Recognizing the importance of human-like robotic behavior for successful collaboration, R2H studies use models of human behavior or observations of H2H data to plan robot giver motion. However, R2H studies have been limited in object counts. In this work, we use the Human-Object-Human (HOH) dataset, consisting of H2H interactions performed by 20 giver-receiver pairs with 136 objects, to conduct an R2H study with 34 objects. We teleoperate a Kinova Gen3 manipulator to grip an object as grasped by an HOH human giver, and program it to automatically transport and orient the object to a participant by mimicking the HOH giver's trajectory and transfer pose. We survey participants on safety, naturalness, and preferred choice over linear trajectory and random orientation baselines. We find that transfer pose influences perceptions of naturalness, with HOH poses showing higher naturalness ratings. Participants prefer handovers with HOH end poses when asked to pick their preferred interaction.