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. In this paper, we explore two key challenges of integrating tactile sensing into intelligent systems for multimodal reasoning: (i) insufficient modeling of dynamic tactile signals, which restricts reasoning over temporally evolving properties, and (ii) hallucination in tactile foundation models caused by the absence of explicit reasoning mechanisms, leading to unstable real-world inference.
arXiv:2606. 11637v1 Announce Type: new Abstract: Touch is a key modality for embodied agents to understand the physical world.
By Kailin Lyu, Di Wu, Pengwei Zhang, Yuhang Zheng, Yingxin Lai, Long Xiao, Kangyi Wu, Pengna Li, Chen Gao, Lianyu Hu, Xiaobin Hu, Jie Hao, Ce Hao, Weihao Yuan, Shuicheng Yan
arXiv:2606. 31451v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities.
By Jiahang Tu, Fengyu Yang, Chenyang Ma, Xihang Yu, Ziyao Zeng, Shaokai Wu, Hanbin Zhao, Zhi Tao, Chao Zhang, Hui Qian, Alex Wong
arXiv:2608.29601v2 Announce Type: replace-cross
Abstract: We present $N_0$-Foundation, a paradigm for tactile-enabled embodied manipulation, which integrates tactile sensing hardware, large-scale mul...
By NeoteAI Team, Fudan TEAI Team
BIDETA is a gradient‑free framework that adapts pretrained tactile models to new sensors using only a few labeled target contacts. It preserves pretrained representations while repairing sensor‑dependent feature neighborhoods through rapid support memory, support‑conditioned spectral graphs, and reliability‑gated recurrence. Experiments on multiple datasets show that BIDETA dramatically improves accuracy and speeds up adaptation compared to prior methods.
By Boheng Liu, Lan Wei, Ziyu Li, Chenghua Duan, Qing Li, Dandan Zhang, Xia Wu
arXiv:2609.21449v1 Announce Type: new
Abstract: World Action Models bring the predictive capabilities of video models into robot action generation, providing a rich foundation for modeling future vis...
By Xuancheng Zhang, Xuetao Liu, Qianying Tang, Jizhe Wang, Zhijing Cheng, Bochen Lin, Haoran Wen, Ming Li, Kun Zhan, Yu Liu