The paper presents a 652,157‑parameter action‑conditioned visuotactile world model designed for lifting tasks, integrating behavior cloning, policy learning in imagination, reactive implicit Q‑learning, and model‑assisted force feedback. Experiments on 120 fresh MuJoCo environments and additional ID environments show that visuotactile dynamics reduce force‑action‑effect mean absolute error from 0.413 N to 0.338 N, and model‑assisted feedback boosts force‑budgeted success from 73.3 % to 93.3 %. Imagined reinforcement learning achieves 11.9 % pooled joint success compared to 25.0 % for reactive IQL, with further stress testing adding 330 executions.
By Qinzhen Ma (Rice University)
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:2610.10283v1 Announce Type: cross
Abstract: Humans can grasp everyday objects with almost perfect success rates using fingertip tactile feedback, yet much of the robotic grasping literature emp...
By Ken Nakahara, Aleksei Buvailik, Prokhor Kotov, Roberto Calandra
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:2610.28457v1 Announce Type: cross
Abstract: General-purpose multimodal models have shown promise as direct robot action policies, yet whether they grasp the physics underlying real-world intera...
By Ziyong Wang, Yupei Lin, Runxuan Huang, Liang Lin, Dongyu Zhang, Guangrun Wang
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.
By Ji Pei
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
The study evaluated 281 imitation‑learning policies across seven families in 1,700 real‑robot trials of RJ45 connector grasping and insertion with a five‑fingered hand. Controlled ablations revealed that observation and action representations—such as ROI cropping, robot‑base framing, and point‑encoder normalization for point‑cloud policies, and absolute action prediction for vision‑language‑action policies—drastically improved success rates. Among 67 offline metrics, only five measures of predicted‑motion smoothness and directness correlated reliably with real‑world success, indicating that offline metrics can help shortlist policies but cannot replace actual robot testing.
By Ammar N. Abbas, Aayush Jain, Prashanth Viswanath, Nivesh Gadipudi, Giulia Vilone, Jim Gibbons
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
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. 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
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.
By 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