Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper investigates the impact of gripper design on the quality of human demonstrations for robot learning, specifically in the context of opening sterile medical packaging. They compared three conditions: distributed load grippers, concentrated load grippers, and bare hands, evaluating task performance through success rate, completion time, damage, and perceived workload. Results showed that concentrated load grippers improved performance compared to distributed load grippers, but bare hands still outperformed both, highlighting the need for improved gripper ergonomics.
Handheld grippers for robot learning can hinder rather than help, as even the best gripper design tested still underperformed bare hands in a sterile bandage opening task.
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.