# Touch2Trace：用于灵巧线缆追踪的触觉驱动模仿学习

> 原文链接：https://robotics.hellobike.com/papers/2026-09-14-2609-15921
> 频道：papers | 更新日期：2026-09-14 | 标签：arxiv, cs.RO
> 核心摘要：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。

# Touch2Trace：用于灵巧线缆追踪的触觉驱动模仿学习

::: v-pre
> 原文链接：[Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing](https://arxiv.org/abs/2609.15921)（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
:::

## 原始参考来源 (Sources)

- [arXiv 2609.15921：Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing](https://arxiv.org/abs/2609.15921)