Influence of Gripper Design on Human Demonstration Quality for Robot Learning

2026-03-17

AI Summary

This study addresses the challenge of poor demonstration quality and inefficiency in medical robot learning from human demonstrations, often caused by suboptimal handheld gripper designs that hinder human–robot interaction. For the first time, it systematically quantifies the impact of different gripper ergonomics on human performance during a bandage-unwrapping task, comparing centralized-load and distributed-load grippers against barehand manipulation. Leveraging the UMI robotic platform, the evaluation integrates both subjective cognitive workload (assessed via the NASA-TLX questionnaire) and objective metrics—including task success rate, completion time, and object damage. Results demonstrate that while the centralized-load design outperforms the distributed-load variant, both gripper types are significantly inferior to barehand operation, underscoring the critical need for ergonomic optimization of demonstration interfaces to enhance imitation learning quality in medical robotics.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Interaction Techniques and Devices
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

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.

Problem

• gripper design
• human demonstration
• robot learning
• medical packaging
• usability

Innovation

• gripper design
• learning from demonstration
• human-robot interaction
• medical robotics
• ergonomics