الملخص
Dexterous manipulation of deformable objects demands continuous fingertip-level regulation of pressure, friction, and incipient slip. We study one of the most challenging cases: dexterous cable tracing, feeding a cable through the hand with repeated pinch-and-curl motions of the thumb and index finger. We introduce Touch2Trace, a tactile-driven imitation-learning system for this task, and provide, to our knowledge, the first systematic real-world characterization of how encoder pretraining, control rate, temporal context, and spatial resolution each shape policy performance. The winning learning recipe combines a tactile encoder pretrained for a custom 32 x 32 piezoresistive sensor (TacV5) via self-supervised learning with a lightweight transformer policy trained on teleoperated demonstrations via behavior cloning, deployed at 60 Hz on a Tesollo DG-5F hand. Tactile feedback without vision or explicit cable-state estimation significantly improves tracing performance versus a proprioception-only baseline: from 0.2 cm to 20.1 cm mean distance and 0% to 93% success rate, with zero-shot transfer to unseen cables and routing conditions. The results quantify the influence of key parameters in tactile-driven systems for reliable dexterous deformable object manipulation.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Grimaldi, M., Klee, D., Chen, Z., Jian, T., Lee, W., Lu, W., Yu, T., & Nabi, S. (2026). Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing. https://omanscience.com/ar/articles/touch2trace-tactile-driven-imitation-learning-for-dexterous-cable-tracing
MLA 9
Grimaldi, Matteo, et al. "Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing." https://omanscience.com/ar/articles/touch2trace-tactile-driven-imitation-learning-for-dexterous-cable-tracing.
شيكاغو (المؤلف–التاريخ)
Grimaldi, Matteo, David Klee, Ziling Chen, Tong Jian, Wonju Lee, Wenjie Lu, Tao Yu, and Saleh Nabi. 2026. "Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing." https://omanscience.com/ar/articles/touch2trace-tactile-driven-imitation-learning-for-dexterous-cable-tracing.
هارفارد
Grimaldi, M., Klee, D., Chen, Z., Jian, T., Lee, W., Lu, W., Yu, T. and Nabi, S. (2026) 'Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing', Available at: https://omanscience.com/ar/articles/touch2trace-tactile-driven-imitation-learning-for-dexterous-cable-tracing.
فانكوفر
Grimaldi M, Klee D, Chen Z, Jian T, Lee W, Lu W, et al. Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing. https://omanscience.com/ar/articles/touch2trace-tactile-driven-imitation-learning-for-dexterous-cable-tracing
IEEE
M. Grimaldi, D. Klee, Z. Chen, T. Jian, W. Lee, W. Lu, T. Yu, and S. Nabi, "Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing," https://omanscience.com/ar/articles/touch2trace-tactile-driven-imitation-learning-for-dexterous-cable-tracing.