发表机构
Cornell Univeristy; Meta Reality Labs; University of Washington（康奈尔大学; Meta 现实实验室; 华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对触觉手套因手部姿势变化产生伪像致最小可检测力提高的问题，引入无需修改手套的算法框架，利用姿势信息减轻伪像，经多手套设计和用户验证，有效降低了最小可检测力，提升了手套在相关应用中的可用性。
AI 中文摘要
触觉手套对手部与物体交互时的接触和力进行数字化，助力机器人在灵巧操作、遥操作及示范学习等方面的应用。因其设计柔软灵活舒适，对接触力敏感且不可避免地受手部姿势变化影响，导致与姿势相关的伪像（PRAs），在低力范围问题尤其突出，会导致接触检测错误或延迟，提高手套的最小可检测力（MDF）。本文刻画了与姿势和力相关的PRAs，基于此引入一个无需修改手套的算法框架，利用手部姿势信息减轻PRAs。姿势感知力估计模型通过一个明确考虑姿势引起的传感器变形的残差预测分支增强触觉到力的管道。在3种手套设计和15名用户中验证该方法，MDF分别降低了10.4%、12.2%和18.3%，所有评估指标均有持续改善。该方法为提高触觉手套在数据收集和各种机器人应用中的可用性提供了实用途径。
英文摘要
Tactile gloves digitize contact and force during hand-object interactions, enabling robotics applications in dexterous manipulation, teleoperation, and learning from demonstration. To preserve hand dexterity and capture the nuances of natural interactions, these gloves and the integrated tactile sensors are designed to be soft, flexible, and comfortable. However, such flexible sensors are sensitive not only to contact forces but also unavoidably to hand pose changes, resulting in pose-related artifacts (PRAs). PRAs are especially problematic in the low-force range, resulting in misdetections or late-onset detections of contact, which raises the minimum detectable force (MDF) of the glove. In this work, we characterize the PRAs in relation to pose and force. Building on these insights, we introduce a glove-agnostic algorithmic framework that leverages hand pose information, which is increasingly available, to mitigate PRAs without glove modifications. Our pose-aware force estimation model augments tactile-to-force pipelines with a residual prediction branch that explicitly accounts for pose-induced sensor deformations. We validate our approach across 3 glove designs and 15 users, reducing MDF by 10.4%, 12.2%, and 18.3%, with consistent improvements across all evaluated metrics. This method provides a practical path to improving the usability of tactile gloves in data collection and diverse robotic applications.
