VT-MUSE is a multimodal unified sequential representation learning framework for visuotactile manipulation. It uses a two‑stage approach: first, modality‑specific encoders are jointly adapted with cross‑modal temporal alignment and masked‑view consistency; second, a conditional variational latent model processes masked visual sequences and full tactile histories, with auxiliary decoders reconstructing recent visual observations and predicting tactile depth changes. The resulting representation is fed into a lightweight Transformer policy via gated cross‑attention, achieving an 11‑percentage‑point improvement over the strongest baseline in simulation and significant gains in real‑world experiments.
By Congsheng Xu, Qiaochu Yang, Fangyuan Shi, Yifan Han, Baijun Chen, Yiming Wang, Haonan Zhao, Daolin Ma, Xiaokang Yang, Hesheng Wang
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:2609.24976v1 Announce Type: cross
Abstract: Dexterous manipulation depends on contact dynamics that are often only partially observable from vision. Recent World-Action Models (WAMs) couple pre...
By Haoran Yuan, Zekai Wang, Boning Shao, Haoran Lu, Trevor Darrell, Ismini Lourentzou, Wei Zhan
The paper introduces BIFTA, a Brain‑Inspired Few‑Shot Tactile Adaptation framework that enables a frozen encoder to adapt quickly to an unknown tactile sensor using only a small labeled support set. It preserves pretrained representations via dual‑view statistical memory, builds support‑conditioned spectral graphs to correct sensor‑dependent feature neighborhoods, and employs uncertainty‑gated recurrent propagation to reinforce reliable cross‑query evidence. Benchmarks on three tactile datasets demonstrate that BIFTA dramatically improves adaptation performance, achieving an 87.09% mean Sparsh accuracy on SITR with just 10% labeled data—an increase of 47.22 percentage points over the best prior method.
By Boheng Liu, Ziyu Li, Xia Wu
TouchSight is a monocular egocentric vision framework that predicts dense full-hand contact forces from video. It uses 500 hours of pressure‑glove recordings and hand‑object interaction data, and introduces TwinTouch‑20H, a dataset of 20 hours of paired visual data where generative models render gloved recordings as bare‑hand observations while preserving tactile labels. The system outperforms prior methods on OakInk2, generalizes qualitatively to natural bare‑hand egocentric videos from unseen datasets, and improves consistently as glove supervision scales.
By Danyan Zhou, Jinxuan Lu, Jiawei Lin, Tianxing Chen, Chuqiao Lyu, Wenbo Ding
arXiv:2606. 12109v2 Announce Type: replace-cross Abstract: Pre-trained Vision-Language-Action (VLA) models provide useful semantic and spatial priors, yet their parallel-gripper action interfaces do not specify how those priors should be realized by a dexterous hand.
By Chuanke Pang, Junyi Huang, Zhijun Zhao, Yaobing Wang, Kun Xu, Xilun Ding
arXiv:2606. 31694v1 Announce Type: cross Abstract: For robots manipulating open-world objects, tactile representations must generalize to unseen materials.
By Jingbo He, Michael F\"arber, Roberto Calandra
arXiv:2607. 04820v1 Announce Type: new Abstract: Decoding hand kinematics from surface electromyography (EMG) is a core challenge in wearable biosignal processing with clinical relevance for prosthetic control and motor rehabilitation.
By Sofia Gilardini, Chenfei Ma, Kianoush Nazarpour
arXiv:2609.14783v1 Announce Type: cross
Abstract: Robots need touch to manipulate objects safely and reliably, as many properties, such as softness, texture, and contact stability, are hard to infer...
By Mashood M. Mohsan, Muhayy Ud Din, Binzhao Xu, Ahmad Abubakar, Irfan Hussain
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
TouchSight is a monocular egocentric vision framework that predicts dense full-hand contact forces without tactile sensors. It uses 500 hours of pressure-glove data and a 20-hour TwinTouch-20H dataset where generative models render gloved recordings as bare-hand videos, bridging the appearance gap. The system outperforms previous methods on OakInk2, generalizes to unseen natural bare-hand egocentric videos, and improves as glove supervision increases.
arXiv:2608.29601v1 Announce Type: cross
Abstract: We present $\mathcal{N}_0$-Foundation, a paradigm for tactile-enabled embodied manipulation, which integrates tactile sensing hardware, large-scale m...
By NeoteAI Team, Fudan TEAI Team