外观
论文arXiv 2609.159211 个月前
Touch2Trace：用于灵巧线缆追踪的触觉驱动模仿学习
原文链接：Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing（arXiv 2609.15921 · 发布日期：2026-09-14）
Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing
Touch2Trace用触觉编码器与轻量Transformer模仿学习解决灵巧线缆追踪，并在Tesollo DG-5F真手上验证。
核心要点
- 面向可变形物体灵巧操作中需连续调节指尖压力、摩擦和初始滑移的线缆追踪任务。
- 方法结合为TacV5自监督预训练的触觉编码器与基于遥操作示范行为克隆训练的轻量Transformer策略。
- 仅用触觉反馈、无视觉或显式线缆状态估计，相比本体感知基线显著提升追踪距离和成功率。
- 研究系统量化了编码器预训练、控制频率、时间上下文和空间分辨率对策略性能的影响。
验证范围与局限：验证在真实Tesollo DG-5F手上进行，任务为线缆追踪，并包含对未见线缆和走线路径条件的零样本迁移。
英文摘要 (Abstract)
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.
作者：Matteo Grimaldi, David Klee, Ziling Chen, Tong Jian, Wonju Lee, Wenjie Lu, Tao Yu, Saleh Nabi
来源
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