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

机器人触觉示范采集昂贵且缓慢，促使研究者使用更便宜的人类触觉手套。然而人类电容/压阻手套与机器人触觉传感器在换能原理、布局、空间分辨率和动态响应上根本不同，直接对齐原始通道是病态问题。TACROSS 在接触事件层面而非原始数值层面对齐触觉流；硬件端包含五层压阻手套、285 个 sensing points 和 10.86 美元 BOM；学习端用 canonicalizer、残差 adapter 和跨手指时序 Transformer 得到 256 维共享 tactile latent。机器人示范是唯一动作真值，人类示范用于触觉表示学习和置信度加权辅助监督。四项接触密集任务上，系统相对传统遥操作取得 3.5 倍采集效率提升，采集设备成本下降 95.7%。

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.