# Tactile-JEPA：面向分布式触觉传感器的拓扑感知自监督表示学习

> 原文链接：https://robotics.hellobike.com/papers/2026-09-21-2609-24385
> 频道：papers | 更新日期：2026-09-21 | 标签：arxiv, cs.RO
> 核心摘要：Tactile-JEPA利用传感器连接图进行拓扑感知自监督预训练，并在三个触觉数据集上验证下游效果。

# Tactile-JEPA：面向分布式触觉传感器的拓扑感知自监督表示学习

::: v-pre
> 原文链接：[Tactile-JEPA: Topology-Aware Self-Supervised Representation Learning for Distributed Tactile Sensors](https://arxiv.org/abs/2609.24385)（arXiv 2609.24385 · 发布日期：2026-09-21）

**Tactile-JEPA: Topology-Aware Self-Supervised Representation Learning for Distributed Tactile Sensors**

Tactile-JEPA利用传感器连接图进行拓扑感知自监督预训练，并在三个触觉数据集上验证下游效果。

## 核心要点

- 现有触觉编码器常从原始噪声信号训练，视觉式SSL难适配稀疏不规则电子皮肤。
- 方法预测被遮蔽感知单元的嵌入，并用传感器连接图指导空间遮蔽。
- 双尺度遮蔽同时捕获局部接触细节与触觉表面的全局状态。
- 在三个数据集上，力估计误差降6.3%，手内朝向误差降20.8%，代码已开源。

**验证范围与局限**：验证范围为三个数据集，涵盖磁性和压阻传感器、不同机器人具身及单传感器和成对传感器配置。

## 英文摘要 (Abstract)

Tactile sensing is an essential modality for robots performing contact-rich, dexterous manipulation, particularly under visual occlusion. While pre-trained image encoders are standard in robot learning pipelines, tactile encoders are still commonly trained from scratch from raw, noisy signals, which might limit their expressivity. Existing self-supervised learning (SSL) approaches focus predominantly on vision-based tactile sensors, leaving distributed electronic skins largely unaddressed. These sensors, however, have a distinctive property: their sensing elements are sparse and irregularly arranged over the surface they cover, which makes direct reuse of visual SSL methods suboptimal. We present Tactile-JEPA, an efficient self-supervised pre-training method that uses the spatial arrangement of tactile sensors to learn topology-aware representations. Specifically, it is trained to predict the embeddings of masked sensing elements from the unmasked remainder, using the sensor connectivity graph to guide spatial masking. Our analysis shows that effective tactile representations require capturing both local contact details and the global state of the tactile surface, which we achieve through dual-scale masking. Across three diverse datasets spanning magnetic and piezoresistive sensors, different robot embodiments, and single- and paired-sensor configurations, Tactile-JEPA reduces force estimation error by 6.3% and in-hand orientation error by 20.8% over the prior state-of-the-art, with consistent gains in other downstream applications, including policy learning. Overall, our results demonstrate that the benefit of tactile sensing depends critically on the quality of encoder pre-training, a problem which Tactile-JEPA addresses directly. Code is available at https://github.com/E-Kovtun/tactile.

作者：Elizaveta Kovtun, Matvey Konovalov, Andrey Sakhovskiy, Semen Budennyy
:::

## 原始参考来源 (Sources)

- [arXiv 2609.24385：Tactile-JEPA: Topology-Aware Self-Supervised Representation Learning for Distributed Tactile Sensors](https://arxiv.org/abs/2609.24385)