Tactus: Tactile & Pose Sensing Glove for Natural Dexterity Data Collection
作者: Li Guan, Rachael Granberry, Sahar Aseeri, Sahar Rostami, Taylor Niehues, Sai Dhanooshi Marri, Mia Huang, Dingtian Zhang, Ziyi Kou, Jason Pursel, Eric Whitmire, Vatsal Mehta, Ashish Deshpande, Tristan Trutna, Reed Belcher, Emelia Black, Julian Blake, Roger Boldu, Sean Bonaventure, Einar Boyovich, Jordan Brennan, Elizabeth Casillas, David Christensen, Tyler Christensen, Nicholas Colonnese, Stuart Dealey, Gaige DeHaven, Katie Doran, Chris Doughty, Robert Drury, Silvia Espino, Gabriel Geyne, Beth Goodrich, Kyra Gould, Joe Grisso, Susana Guerrero, Andrew Gwozdziowski, Monica Ha, Ehsan Hajiesmaili, Kate Healy, Rose Hope, Michael Hurst, Claire Ikemiya, Daisy Isibor, Rick James, Yashasvi Joginapally, Samuel Johnson, Tyler Jorgensen, Kristy Jost, Ben Kamath, Slava Karulin, Alexander Keener, Woo Kim, Alison Krauskopf, Ankit Kumar, Joshua Lang, Lizzy Li, Cameron Lynch, Daniel Ma, Hector Martinon, Christina Matos, Keagan McCurdy, Lee Mincey, Stuart Nezlek, Tiffany Nguyen, James O'Hara, Jason Osborne, Jacky Pai, Roham Pardakhtim, David Perek, Alexandre Poulin, Nick Raines, Salwa Rasheed, Leah Resneck, Kevin Rhodes, Ergys Ristani, Douglas Roberts, Anik Sarker, Harrison Scarborough, Jonas Schmidtler, Brian Shieh, Benjamin Shuman, Garrett Simard, Forrest Smith, Udara Somarathna, Eliot Sosin, Jonathan Stewart, Kristine Strom, Adam Swett, Hal Talbot-Mantooth, Julia Tanner, Sai Sharan Thirunagari, Curtis Torrey, Simge Uzun, Eileen Wachs, Rylan Wasson, Henry Wei, Jesse White, Beau Woodbyrne, Antonio Woods, Noah Young, Chandler Yount, Joseph Zhang, Sean Keller
分类: cs.RO, cs.AI, cs.AR, cs.LG
发布日期: 2026-10-29
备注: 9 pages
💡 一句话要点
提出Tactus手套以解决自然操作数据收集问题
🎯 匹配领域: 支柱一：机器人控制 (Robot Control) 支柱二：RL算法与架构 (RL & Architecture) 支柱四：生成式动作 (Generative Motion)
关键词: 触觉传感 手部姿态跟踪 机器人操作 可穿戴设备 数据收集
📋 核心要点
- 现有可穿戴设备在自然操作数据收集方面存在不足，无法同时实现精准的接触力和手部姿态跟踪。
- Tactus手套通过优化3D针织结构、布置触觉传感器和IMU融合跟踪，提供了高效的自然操作数据收集方案。
- 实验结果表明，Tactus的最小可检测力为0.59 N，且在姿态跟踪中相对误差仅为11.0 mm，显著优于商业基线。
📝 摘要（中文）
收集自然的人类操作演示，准确获取接触力和手部姿态对于机器人灵巧操作策略和基础世界模型学习至关重要。然而，现有可穿戴平台无法同时提供经过校准的指尖力传感和抗遮挡的手部跟踪。本文提出了Tactus，一种纺织集成的传感手套，优化了三大设计要素：薄型、符合人体工学的3D针织结构，战略性布置的电容触觉传感器，以及融合IMU的姿态跟踪。Tactus还包括文档化的校准流程，确保跨手套的一致性和针对个体的校正。我们引入了标准化的基准测试协议，并对Tactus与两种商业触觉手套进行了定量比较，结果显示Tactus在检测力和姿态跟踪方面表现优异。
🔬 方法详解
问题定义：本文旨在解决现有可穿戴平台在自然操作数据收集中的不足，特别是在接触力和手部姿态的准确性与可靠性方面。现有方法无法同时提供经过校准的指尖力传感和抗遮挡的手部跟踪，限制了其在机器人灵巧操作中的应用。
核心思路：Tactus手套的核心思路是通过优化设计，结合纺织材料与先进传感技术，实现高精度的接触力和手部姿态跟踪。设计旨在最小化对自然操作行为的干扰，同时确保数据的准确性和一致性。
技术框架：Tactus的整体架构包括三个主要模块：1) 3D针织手套结构，提供舒适的佩戴体验；2) 布置在关键操作区域的电容触觉传感器，能够准确捕捉接触力；3) 融合IMU的姿态跟踪系统，确保在手部与物体遮挡时仍能保持准确性。
关键创新：Tactus的主要创新在于其纺织集成的设计和校准流程，能够实现跨手套的一致性和个体校正。这使得Tactus在检测力和姿态跟踪方面的性能显著优于现有商业产品。
关键设计：Tactus的设计包括1.5 mm的指尖堆叠厚度，确保手套的薄型和符合人体工学；电容触觉传感器的布置经过实证研究优化，确保在操作相关区域的敏感性；此外，校准流程经过文档化，确保在不同用户和会话之间的数据一致性。
🖼️ 关键图片
📊 实验亮点
实验结果显示，Tactus在95%置信度下的最小可检测力为0.59 N，较商业基线低1.6至2.7倍，且跨参与者一致性达到0.87。姿态跟踪在重度遮挡情况下的相对误差仅为11.0 mm，展现出优异的性能。
🎯 应用场景
Tactus手套在机器人灵巧操作、虚拟现实和人机交互等领域具有广泛的应用潜力。其高精度的接触力和姿态跟踪能力能够为机器人学习和自主操作提供更为丰富的数据支持，推动智能系统的进一步发展。
📄 摘要（原文）
Collecting naturalistic human manipulation demonstrations with accurate contact force and hand pose is essential for robot dexterous manipulation policy and foundational world model learning - yet no existing wearable platform provides both calibrated fingertip force sensing and occlusion-resilient hand tracking in a form factor that preserves natural manipulation behavior. We present Tactus, a textile-integrated sensing glove that co-optimizes three design pillars: (1) a thin, conformal 3D-knit form factor (1.5 mm fingertip stackup) designed to minimize interference with natural manipulation, (2) strategically placed capacitive tactile sensors at manipulation-relevant regions informed by empirical contact studies, and (3) IMU-fused pose tracking that maintains accuracy through the hand-object occlusions where manipulation data is most valuable. Tactus includes a documented calibration pipeline for cross-glove consistency and per-session calibration for subject-dependent correction. To enable objective comparison, we introduce standardized benchmarking protocols for tactile force regression and hand pose estimation, and apply them to Tactus and two commercially available tactile gloves - to our knowledge the first quantitative cross-platform evaluation of this kind. Tactus resolves a minimum detectable force of 0.59 N at 95% confidence, 1.6 - 2.7x lower than the commercial baselines, and with 0.87 cross-participant consistency, while our pose tracking sustains 11.0 mm fingertip keypoint relative error during manipulation, when occlusion is heaviest. We show that these combined signals support object property estimation, slip detection, and representative recording of hand-object interaction - yielding demonstration data with the fidelity required for contact-aware manipulation policies and world models.