arXiv：cs.LG· Bo Chen, Huanzhang Hu, Junyang Ma, Bo Yue, Fangdi Yu, Haijier Chen, Xianxin Lai, Shuyu Pan, Zhen Yang, Xiaoquan Sun, Wenze Cui, Zhongliang Jiang, Shaopeng Liu, Jiayu Chen·· 4 小时前
TACROSS：面向异构触觉传感器的高效低成本可扩展人类触觉系统，助力灵巧机器人学习
TACROSS: An Efficient and Low-Cost Scalable Human Touch System Across Heterogeneous Tactile Sensors for Dexterous Robot Learning
AI 导读
TACROSS 通过将人类触觉与机器人触觉在接触事件层面而非原始传感器数值上对齐，实现跨异构触觉传感器的触觉技能迁移。其硬件采用五层压阻式手套，成本 10.86 美元、285 个传感点，并借助时间 Transformer 将异构信号映射至 256 维共享触觉隐空间。
正文
Authors:Bo Chen, Huanzhang Hu, Junyang Ma, Bo Yue, Fangdi Yu, Haijier Chen, Xianxin Lai, Shuyu Pan, Zhen Yang, Xiaoquan Sun, Wenze Cui, Zhongliang Jiang, Shaopeng Liu, Jiayu Chen
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: this https URL.
| Subjects: | Robotics (cs.RO); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11945 [cs.RO] |
| (or arXiv:2610.11945v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11945 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bo Chen [view email]
[v1]
Thu, 8 Oct 2026 13:34:31 UTC (35,130 KB)
来源：arXiv：cs.LG · arxiv.org