How Robot Kinematics Influence Human Performance in Virtual Robot-to-Human Handover Tasks

TL;DR

This study investigates how different robot kinematics and interaction design choices in VR affect human performance during robot-to-human handover tasks, emphasizing the importance of visual cues and smooth trajectories.

Contribution

It provides new insights into how robot motion profiles and visual information influence human motor performance in collaborative tasks.

Findings

Humans perform better with early visual cues about object motion.

Smooth, human-like robot trajectories improve predictive accuracy.

Designing interactions to leverage natural human motion detection reduces cognitive load.

Abstract

Recent advancements in robotics have increased the possibilities for integrating robotic systems into human-involved workplaces, highlighting the need to examine and optimize human-robot coordination in collaborative settings. This study explores human-robot interactions during handover tasks using Virtual Reality (VR) to investigate differences in human motor performance across various task dynamics and robot kinematics. A VR-based robot handover simulation afforded safe and controlled assessments of human-robot interactions. In separate experiments, four potential influences on human performance were examined (1) control over task initiation and robot movement synchrony (temporal and spatiotemporal); (2) partner appearance (human versus robotic); (3) robot velocity profiles (minimum jerk, constant velocity, constant acceleration, and biphasic); and (4) the timing of rotational object motion. Findings across experiments emphasize humans benefit from robots providing early and salient visual information about task-relevant object motion, and advantages of human-like smooth robot trajectories. To varying degrees, these manipulations improved predictive accuracy and synchronization during interaction. This suggests that human-robot interactions should be designed to allow humans to leverage their natural capabilities for detecting biological motion, which conversely may reduce the need for costly robotic computations or added cognitive adaptation on the human side.