Influence of Gripper Design on Human Demonstration Quality for Robot Learning
This work addresses the challenge of improving robot learning from human demonstrations in healthcare settings, though it is incremental as it focuses on ergonomic refinements of existing tools.
The study evaluated how different gripper designs affect human demonstration quality for robot learning, specifically in a bandage opening task, finding that concentrated load grippers improved performance over distributed ones but were still slower and less effective than bare hands.
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.