Impact of Object Weight in Handovers: Inspiring Robotic Grip Release and Motion from Human Handovers

TL;DR

This paper investigates how object weight affects human handover behavior and develops adaptive robotic strategies to improve robot-human handovers by mimicking human motion and grip release patterns based on object weight.

Contribution

It introduces an adaptive grip-release strategy for robots, analyzes the influence of object weight on human motion, and creates a new handover dataset including various weights.

Findings

Adaptive strategies outperform baseline in naturalness and efficiency.

Object weight significantly influences human grip and motion during handovers.

Robots can better mimic human behavior by considering object weight.

Abstract

This work explores the effect of object weight on human motion and grip release during handovers to enhance the naturalness, safety, and efficiency of robot-human interactions. We introduce adaptive robotic strategies based on the analysis of human handover behavior with varying object weights. The key contributions of this work includes the development of an adaptive grip-release strategy for robots, a detailed analysis of how object weight influences human motion to guide robotic motion adaptations, and the creation of handover-datasets incorporating various object weights, including the YCB handover dataset. By aligning robotic grip release and motion with human behavior, this work aims to improve robot-human handovers for different weighted objects. We also evaluate these human-inspired adaptive robotic strategies in robot-to-human handovers to assess their effectiveness and performance and demonstrate that they outperform the baseline approaches in terms of naturalness, efficiency, and user perception.