论文/arXiv 2610.11945·20 天前

TACROSS：面向灵巧机器人学习、跨异构触觉传感器的高效低成本可扩展人类触觉系统

TACROSS通过接触事件级对齐异构触觉流，将低成本人类触觉手套数据迁移到机器人，并在四个接触丰富操作任务上验证。

AI 整理

• 硬件零部件
• 数据与采集
• 具身模型

原文链接：TACROSS: An Efficient and Low-Cost Scalable Human Touch System Across Heterogeneous Tactile Sensors for Dexterous Robot Learning（arXiv 2610.11945 · 发布日期：2026-10-08）

TACROSS: An Efficient and Low-Cost Scalable Human Touch System Across Heterogeneous Tactile Sensors for Dexterous Robot Learning

TACROSS通过接触事件级对齐异构触觉流，将低成本人类触觉手套数据迁移到机器人，并在四个接触丰富操作任务上验证。

核心要点

• 动机：机器人触觉示教采集昂贵且缓慢，低成本人类触觉手套有助于规模化采集。
• 方法：在接触事件层对齐触觉流，用适配器和时序Transformer形成共享潜表示。
• 策略学习：机器人示教提供动作真值，人类示教用于表征学习和置信加权辅助监督。
• 实验：四个接触丰富任务中，相比传统遥操作效率提升3.5倍，设备成本降低95.7%。

验证范围与局限：验证范围为四个接触丰富的操作任务；摘要未说明仿真/真机或机器人数量。

英文摘要 (Abstract)

Collecting tactile demonstrations on robots is costly and slow, motivating the use of lower-cost human tactile gloves for scalable data collection. However, human capacitive/piezoresistive gloves and robotic tactile sensors differ fundamentally in transduction principle, sensor layout, spatial resolution, and dynamic response, making alignment of raw sensor channels ill-posed. To address this problem, we present TACROSS, a scalable system for learning from human touch and transferring it to robots that bridges this heterogeneity by aligning tactile streams at the level of contact events rather than raw sensor values. The hardware component of TACROSS integrates a piezoresistive glove with five layers and a cost of USD 10.86 with 285 sensing points. To align contact semantics, we design canonicalizers and residual adapters that map heterogeneous signals into a shared tactile latent with 256 dimensions via a temporal Transformer with attention across fingers. We further introduce a robot-grounded policy learning scheme in which robot demonstrations provide the sole source of ground-truth action supervision, while human demonstrations support tactile representation learning and provide confidence-weighted auxiliary supervision through valid retargeted hand targets. We evaluate our system on four contact-rich manipulation tasks. Compared to conventional teleoperation, our proposed system achieves a 3.5-fold efficiency improvement while reducing demonstration acquisition equipment cost by 95.7%. We will open-source the TACROSS hardware and software system and publicly release a tactile dataset comprising over 150 hours of recordings. Project page: https://tacross-touch-project.github.io/.

作者：Bo Chen, Huanzhang Hu, Ma, Junyang, Bo Yue, Fangdi Yu, Haijier Chen, Xianxin Lai, Shuyu Pan, Zhen Yang, Xiaoquan Sun

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.11945：TACROSS: An Efficient and Low-Cost Scalable Human Touch System Across Heterogeneous Tactile Sensors for Dexterous Robot Learning访问 2026-10-21