# 触觉引导的机器人学习：从表征学习到基础模型

> 原文链接：https://robotics.hellobike.com/papers/2026-10-14-2610-20134
> 频道：papers | 更新日期：2026-10-14 | 标签：arxiv, cs.RO
> 核心摘要：本文从学习中心视角综述触觉引导技能学习，梳理触觉在学习与控制流程中的作用及相关文献资源。

# 触觉引导的机器人学习：从表征学习到基础模型

::: v-pre
> 原文链接：[Tactile-Guided Robot Learning: From Representation Learning to Foundation Models](https://arxiv.org/abs/2610.20134)（arXiv 2610.20134 · 发布日期：2026-10-14）

**Tactile-Guided Robot Learning: From Representation Learning to Foundation Models**

本文从学习中心视角综述触觉引导技能学习，梳理触觉在学习与控制流程中的作用及相关文献资源。

## 核心要点

- 动机在于触觉正成为鲁棒、自适应、灵巧操作的核心，但现有综述对学习式触觉操作综合不足。
- 方法按触觉在学习与控制流程中的功能角色组织，考察表征与策略学习、基础模型、预测和自适应控制等方向。
- 综述识别了可迁移物理表征、语义推理与接触级控制融合、异构数据和硬件扩展等共同原则与挑战。
- 论文提供所评述文献及相关资源的配套数据库，并展望触觉基础模型和世界模型方向。

**验证范围与局限**：本文为综述，范围限于所回顾的触觉引导技能学习文献与相关资源。

## 英文摘要 (Abstract)

Tactile sensing is becoming central to robust, adaptive, and dexterous robotic manipulation. Beyond its traditional role in contact detection and reactive control, touch is increasingly used to learn transferable representations, condition manipulation policies, and support embodied physical reasoning. Yet existing surveys largely emphasize tactile hardware and perception, leaving the rapid shift toward learning-based tactile manipulation insufficiently synthesized. This paper reviews tactile-guided skill learning from a learning-centric perspective, organized by the functional role of touch across the learning and control pipeline. We examine tactile representation and policy learning, tactile foundation models, predictive and adaptive control, and generalization across objects, tasks, sensors, embodiments, and simulation-to-real transfer. Across these directions, we identify common design principles and remaining challenges in learning transferable physical representations, integrating semantic reasoning with contact-level control, and scaling tactile learning across heterogeneous data and hardware. Finally, we outline emerging directions toward tactile foundation and world models that can predict, reason about, and adapt physical interactions. Together, this review characterizes the transition of touch from a task-specific feedback signal toward a core modality for general-purpose embodied intelligence. An accompanying database of the reviewed literature and associated resources is available at https://sites.google.com/view/touch-in-robot-learning-review.

作者：Kiyanoush Nazari, Luchen Li, Haoshu Fang, Nima Fazeli, Zhibin Li, Fumiya Iida A, Thomas George Thuruthel
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## 原始参考来源 (Sources)

- [arXiv 2610.20134：Tactile-Guided Robot Learning: From Representation Learning to Foundation Models](https://arxiv.org/abs/2610.20134)