Influence of Gripper Design on Human Demonstration Quality for Robot Learning
- URL: http://arxiv.org/abs/2603.17189v1
- Date: Tue, 17 Mar 2026 22:36:09 GMT
- Title: Influence of Gripper Design on Human Demonstration Quality for Robot Learning
- Abstract summary: We evaluate the effectiveness of Universal Manipulation Interface (UMI) in demonstrating a bandage opening task.<n>Concentrated load grippers improved performance relative to distributed load grippers but remained substantially slower and less effective than hands.<n>These results underscore the importance of ergonomic and mechanical refinements in handheld grippers to reduce user burden and improve demonstration quality.
- Score: 0.5872014229110214
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
Related papers
- Pre-training Visual Dexterity in Simulation [67.17477115017896]
We introduce a pre-training framework for dexterous manipulation that uses data entirely collected in simulation.<n>With the help of five operators, we collect 75 hours of multi-task dexterous manipulation over one week, and use it to pre-train a causal transformer on a sequence modeling objective.<n>We find that our approach outperforms training behavior cloning policies from scratch, showing that simulation teleoperation is a viable pre-training source for real-world dexterous manipulation.
arXiv Detail & Related papers (2026-08-16T20:21:33Z) - Give me scissors: Collision-Free Dual-Arm Surgical Assistive Robot for Instrument Delivery [4.187693745535412]
During surgery, scrub nurses are required to frequently deliver surgical instruments to surgeons.<n>Existing research on robotic scrub nurses relies on predefined paths for instrument delivery.<n>We present a collision-free dual-arm surgical assistive robot capable of performing instrument delivery.
arXiv Detail & Related papers (2026-03-03T03:14:59Z) - Towards Autonomous Instrument Tray Assembly for Sterile Processing Applications [2.5401898591113814]
We present a fully automated robotic system that sorts and structurally packs surgical instruments into sterile trays.<n>The system integrates a vision module, a 6-DOF Staubli TX2-60L robotic arm with a custom dual electromagnetic gripper, and a rule-based packing algorithm that reduces instrument collisions during transport.<n> Experimental evaluations show high perception accuracy and statistically significant reduction in tool-to-tool collisions compared to human-assembled trays.
arXiv Detail & Related papers (2026-02-02T05:46:31Z) - Setup-Invariant Augmented Reality for Teaching by Demonstration with Surgical Robots [49.26692555627371]
We present dV-STEAR, an open-source system that plays back task-aligned expert demonstrations without assuming identical setup joint positions between expert and novice.<n>In a user study, dV-STEAR significantly improved novice performance on tasks from the Fundamentals of Laparoscopic Surgery.<n>Across both tasks, participants using dV-STEAR exhibited significantly more balanced hand use and reported lower frustration levels.
arXiv Detail & Related papers (2025-04-09T08:34:25Z) - Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition [48.65867987106428]
We introduce a novel system for joint learning between human operators and robots.
It enables human operators to share control of a robot end-effector with a learned assistive agent.
It reduces the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks.
arXiv Detail & Related papers (2024-06-29T03:37:29Z) - EfficientPPS: Part-aware Panoptic Segmentation of Transparent Objects
for Robotic Manipulation [1.0819401241801996]
We present EfficientPPS, a neural architecture for part-aware panoptic segmentation.
It provides robots with semantically rich visual information for grasping and ma-nipulation tasks.
We also present an unsupervised data collection and labelling method to reduce the need for human involvement.
arXiv Detail & Related papers (2023-12-21T14:51:23Z) - Intuitive Surgical SurgToolLoc Challenge Results: 2022-2023 [55.40111320730479]
We have challenged the surgical data science community to solve difficult machine learning problems in the context of advanced RA applications.<n>Here we document the results of these challenges, focusing on surgical tool localization (SurgToolLoc)<n>The publicly released dataset that accompanies these challenges is detailed in a separate paper arXiv:2501.09209.
arXiv Detail & Related papers (2023-05-11T21:44:39Z) - Spatiotemporal modeling of grip forces captures proficiency in manual
robot control [5.504040521972806]
This paper builds on our previous work by exploiting Artificial Intelligence to predict individual grip force variability in manual robot control.
Statistical analyses bring to the fore skill specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert.
arXiv Detail & Related papers (2023-03-03T15:08:00Z) - Demonstration-Guided Reinforcement Learning with Efficient Exploration
for Task Automation of Surgical Robot [54.80144694888735]
We introduce Demonstration-guided EXploration (DEX), an efficient reinforcement learning algorithm.
Our method estimates expert-like behaviors with higher values to facilitate productive interactions.
Experiments on $10$ surgical manipulation tasks from SurRoL, a comprehensive surgical simulation platform, demonstrate significant improvements.
arXiv Detail & Related papers (2023-02-20T05:38:54Z) - Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot
Learning [121.9708998627352]
Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off.
This work revisits the robustness-accuracy trade-off in robot learning by analyzing if recent advances in robust training methods and theory can make adversarial training suitable for real-world robot applications.
arXiv Detail & Related papers (2022-04-15T08:12:15Z) - Personalized Rehabilitation Robotics based on Online Learning Control [62.6606062732021]
We propose a novel online learning control architecture, which is able to personalize the control force at run time to each individual user.
We evaluate our method in an experimental user study, where the learning controller is shown to provide personalized control, while also obtaining safe interaction forces.
arXiv Detail & Related papers (2021-10-01T15:28:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.