Agile-WAM is a tactile World Action Model that jointly predicts future visual and tactile states and robot actions for contact‑rich manipulation. It encodes visual and tactile observations into a shared latent space and uses a vision‑tactile‑to‑action flow‑matching process to generate action chunks and future latents. The model introduces multi‑horizon multimodal prediction, leveraging the different timescales of vision and touch, and achieves a 29.4 % improvement in real‑world success rates with 11.9 ms inference latency across nine simulated and five real‑world tasks.
By Hanchu Zhou, Brendan Lynch, Raman Goyal, Dechen Gao, Begum Kasap, Boqi Zhao, Junshan Zhang
DexTouch-WM is an action‑conditioned world model that learns from scalable human touch to predict future RGB observations and bilateral tactile dynamics for dexterous robot manipulation. By using compatible piezoresistive arrays on both human and robot hands and retargeting human motion into the robot action space, the model can be supervised with human interaction data while keeping a fixed amount of real‑robot supervision. Experiments show that adding up to 100 hours of human interaction improves robot‑domain visual, geometric, and contact prediction, and the model can serve as a surrogate environment for policy evaluation and synthetic trajectory generation.
By Yan Qin, Yue Chen, Wenwei Lin, Shujia Liu, Chuqiao Lyu, Kailun Su, Chenze Yu, Ping Luo, Wenbo Ding, Tianxing Chen, Renjing Xu
arXiv:2603. 14604v2 Announce Type: replace-cross Abstract: We propose TacFiLM, a lightweight modality-fusion approach that integrates visual-tactile signals into vision-language-action (VLA) models.
By Charlotte Morissette, Amin Abyaneh, Wei-Di Chang, Anas Houssaini, David Meger, Hsiu-Chin Lin, Jonathan Tremblay, Gregory Dudek
arXiv:2609.09119v1 Announce Type: cross
Abstract: Dexterous manipulation involves contact-rich and fine-grained interactions with the physical world, posing significant challenges for existing vision...
By Yankai Fu, Ning Chen, Junkai Zhao, Heng Zhang, Guocai Yao, Pengwei Wang, Zhongyuan Wang, Shanghang Zhang
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
DeCAL is a vision‑language‑action model designed for dexterous manipulation that incorporates tactile sensing through adaptive visuo‑tactile fusion and latent co‑imagination. It uses a Mixture‑of‑Transformers architecture with specialized experts for understanding, imagination, and action, enabling efficient information flow and dynamic regulation of tactile inputs. Experiments show DeCAL achieves state‑of‑the‑art performance, with a 71% average success rate and 83.4% progress success rate, and generalizes well to unseen scenarios.
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
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:2606. 14981v1 Announce Type: cross Abstract: Inference-time steering adapts pre-trained generative robot policies during deployment by verifying candidate actions before execution.
By Yilin Wu, Zilin Si, Zeynep Temel, Oliver Kroemer, Andrea Bajcsy
TacSushi is a tactile‑grounded, Cosmos3‑based world‑action policy for dexterous sushi manipulation. It encodes RGB, language, and hand state, fusing fingertip tactile data via feature‑wise gated fusion, and learns from future‑consequence predictions while excluding failed actions from imitation. Trained on 340 successful and 50 failed trials, TacSushi achieves 68.3% in‑distribution and 37.5% out‑of‑distribution success, outperforming baselines that lack future‑consequence supervision or use direct tactile concatenation.
By Haodi Hu, Kaen Kogashi, Toshiaki Koike-Akino
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
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