Influence of Gripper Design on Human Demonstration Quality for Robot Learning

TL;DR

This study evaluates how different gripper designs affect the quality of human demonstrations in robot learning, highlighting the importance of ergonomic improvements for healthcare applications.

Contribution

It provides empirical insights into how gripper load distribution impacts demonstration effectiveness in medical tasks, informing design improvements for robotic tools.

Findings

Concentrated load grippers improved task success over distributed load grippers.

Both grippers were less effective than bare hands in demonstration quality.

User workload was higher with mechanical grippers compared to hands.

Abstract

Opening sterile medical packaging is routine for healthcare workers but remains challenging for robots. Learning from demonstration enables robots to acquire manipulation skills directly from humans, and handheld gripper tools such as the Universal Manipulation Interface (UMI) offer a pathway for efficient data collection. However, the effectiveness of these tools depends heavily on their usability. We evaluated UMI in demonstrating a bandage opening task, a common manipulation task in hospital settings, by testing three conditions: distributed load grippers, concentrated load grippers, and bare hands. Eight participants performed timed trials, with task performance assessed by success rate, completion time, and damage, alongside perceived workload using the NASA-TLX questionnaire. Concentrated load grippers improved performance relative to distributed load grippers but remained substantially slower and less effective than hands. These results underscore the importance of ergonomic and mechanical refinements in handheld grippers to reduce user burden and improve demonstration quality, especially for applications in healthcare robotics.