arXiv AIBy Li Guan, Rachael Granberry, Sahar Aseeri, Sahar Rostami, Taylor Niehues, Sai Dhanooshi Marri, Mia Huang, Dingtian Zhang, Ziyi Kou, Jason Pursel, Eric Whitmire, Vatsal Mehta, Ashish Deshpande, Tristan Trutna, Reed Belcher, Emelia Black, Julian Blake, Roger Boldu, Sean Bonaventure, Einar Boyovich, Jordan Brennan, Elizabeth Casillas, David Christensen, Tyler Christensen, Nicholas Colonnese, Stuart Dealey, Gaige DeHaven, Katie Doran, Chris Doughty, Robert Drury, Silvia Espino, Gabriel Geyne, Beth Goodrich, Kyra Gould, Joe Grisso, Susana Guerrero, Andrew Gwozdziowski, Monica Ha, Ehsan Hajiesmaili, Kate Healy, Rose Hope, Michael Hurst, Claire Ikemiya, Daisy Isibor, Rick James, Yashasvi Joginapally, Samuel Johnson, Tyler Jorgensen, Kristy Jost, Ben Kamath, Slava Karulin, Alexander Keener, Woo Kim, Alison Krauskopf, Ankit Kumar, Joshua Lang, Lizzy Li, Cameron Lynch, Daniel Ma, Hector Martinon, Christina Matos, Keagan McCurdy, Lee Mincey, Stuart Nezlek, Tiffany Nguyen, James O'Hara, Jason Osborne, Jacky Pai, Roham Pardakhtim, David Perek, Alexandre Poulin, Nick Raines, Salwa Rasheed, Leah Resneck, Kevin Rhodes, Ergys Ristani, Douglas Roberts, Anik Sarker, Harrison Scarborough, Jonas Schmidtler, Brian Shieh, Benjamin Shuman, Garrett Simard, Forrest Smith, Udara Somarathna, Eliot Sosin, Jonathan Stewart, Kristine Strom, Adam Swett, Hal Talbot-Mantooth, Julia Tanner, Sai Sharan Thirunagari, Curtis Torrey, Simge Uzun, Eileen Wachs, Rylan Wasson, Henry Wei, Jesse White, Beau Woodbyrne, Antonio Woods, Noah Young, Chandler Yount, Joseph Zhang, Sean Keller
Tactus: Tactile & Pose Sensing Glove for Natural Dexterity Data Collection
Tactus is a textile‑integrated glove that combines a thin, conformal 3D‑knit design with strategically placed capacitive tactile sensors and IMU‑fused pose tracking to capture natural human manipulation. It offers a calibrated, cross‑glove consistency pipeline and per‑session subject correction, achieving a minimum detectable force of 0.59 N and fingertip pose error of 11.0 mm even under heavy occlusion. The authors benchmark Tactus against two commercial gloves, presenting the first quantitative cross‑platform evaluation of tactile force regression and hand pose estimation.
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— not written or checked by a human. The full article lives at arXiv AI.
SoTa is a low‑cost, capacitive tactile skin that offers full‑hand coverage for both humans and robots, featuring a shared layout of 202 taxels across finger and palm regions. The multilayer design with fabric electrodes allows in‑house fabrication for under $10 in materials, and the sensor maintains over 97% of its response after 10,000 loading‑unloading cycles and 1,280 tight‑fist folds. Using SoTa, human‑robot co‑training improves in‑distribution success on three contact‑rich manipulation tasks, and adding human demonstrations more than doubles mean success across eight evaluation conditions, from 22.8% to 45.9%, including all out‑of‑distribution scenarios.
By Jingyun Yang, Baiyu Shi, Timothy Yu, Haitian Liu, Alberta Longhini, Weichen Wang, Rika Antonova, Zhenan Bao, Jeannette Bohg
The survey examines dexterous robotic hands in the context of foundation models, covering hardware, data, cross-embodiment transfer, learning, and evaluation from January 2024 to November 2026. It highlights that human data’s value hinges on alignment rather than volume, that shared policies still face embodiment constraints, and that task success alone does not guarantee physical correctness or readiness for deployment. The authors identify eleven open problems and suggest reporting standards to better measure full-stack progress.
arXiv:2609.24660v2 Announce Type: cross
Abstract: Human demonstrations offer a scalable way to collect manipulation data, but their contacts may be unstable or infeasible when transferred to a robot...
By Shengcheng Luo, Xiaoyang Cheng, Hong Ying, Xiaoying Zhou, Jiaming Jiang, Haoran Guo, Wanlin Li, Ziyuan Jiao, Chenxi Xiao
arXiv:2609.15726v1 Announce Type: cross
Abstract: Tactile sensing provides contact information that can be difficult to infer from vision alone, but tactile hardware for dexterous hands has not conve...
By Zhenjie Yang, Yideng Zhang, Dongjie Zhang, Chenyu Jiang, Xianshuai Liu, Yufeng Li, Zuhao Ge, Xingyu Jiao, Zheng Zhang, Kaiyu He, He Wang, Yuwen Zhong, Yi Deng, Muyun Jiang, Xianliang Huang, Haisheng Su, Donghang Zhang, Jian Zhang, Xue Yang, Hongyang Li, Zuxuan Wu, Yu-Gang Jiang, Xiaosong Jia, Junchi Yan
TACROSS is a low‑cost, scalable system that enables robots to learn from human touch by aligning tactile streams at the level of contact events rather than raw sensor values. It combines a $10.86 piezoresistive glove with 285 sensing points and a temporal Transformer that maps heterogeneous signals into a shared 256‑dimensional latent space. The approach uses robot demonstrations for action supervision while human demonstrations aid tactile representation learning, achieving a 3.5‑fold efficiency improvement and a 95.7% reduction in demonstration equipment cost across four manipulation tasks.
By 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
Retargeting human object interaction demonstrations to physics based simulation requires reproducing not only body motion but also the object motion and contacts that make manipulation succeed. However, position only hand trajectories do not specify the contact forces needed to manipulate objects, and directly tracking them can overconstrain contact rich finger behavior.