arXiv AI

RCT: A Robot-Collected Touch-Vision-Language Dataset for Tactile Generalization

arXiv:2606. 31694v1 Announce Type: cross Abstract: For robots manipulating open-world objects, tactile representations must generalize to unseen materials.

arXiv Machine Learning
Jun 11

TacCoRL: Integrating Tactile Feedback into VLA via Simulation

arXiv:2606. 11743v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide strong visual, language, and action priors for robot manipulation, but visual observations alone often miss the local contact state required for contact-rich tasks.

By Siyu Ma, Yuqi Liang, Chang Yu, Yunuo Chen, Hao Su, Yixin Zhu, Yin Yang, Chenfanfu Jiang
arXiv AI
Jul 7

OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies

arXiv:2607. 03723v1 Announce Type: cross Abstract: Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success significantly depends on local force and contact geometry.

By Kelin Yu, Haode Zhang, Harish Ravichandar, Yunhai Han, Ruohan Gao
arXiv AI
Jul 7

TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios

arXiv:2607. 05131v1 Announce Type: new Abstract: Among the five primary human senses, tactile is arguably the most fundamental to survival, as it enables the perception of physical contact and interaction in real-world environments.

By Kailin Lyu, Di Wu, Long Xiao, Jianning Zeng, Jianwei He, Chang Lin, Lianyu Hu, Lin Shu, Jie Hao, Ce Hao