GPT-6-Astra Understands Touch and Outperform Vision--Tactile--Language--Action Models

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1. Très proche Cité par cet article Pas pertinent

   Systematically Exploring the Capabilities of GPT-6 Astra as Embodied Policies

   G. Chen, Xiao-Qi Cheng, Yu Deng et al. · 2026 · Pas encore résumé

   Résumé scientifique

   GPT-6 Astra exhibits a remarkable ability to generate numerical robot actions, extending its role beyond high-level planning. To assess Astra's capabilities as general-purpose embodied policies, we conduct comprehensive evaluations across six domains, examining direct control, cooperation with learned policies, and feedback-driven adaptation. In gripper manipulation, Astra can correct task targets and prepare contact conditions for subsequent policy execution; hybrid control with {\pi}0.5 achieves 48% success on the evaluated RoboDojo subset. In dexterous manipulation, hybrid control achieves 50% success in ten experience-guided DexJoCo trials, while direct in-hand control struggles to coord…

2. Très proche Pas pertinent

   FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation

   Chengbo Yuan, Zi-Cheng Zhang, Mingjie Zhou et al. · 2026 · arXiv.org · Pas encore résumé

   Résumé scientifique

   Despite the success of vision-based generalist robotic policies, existing tactile-based policies remain tied to fixed embodiments and sensor setups. This is because tactile signals are highly heterogeneous across hardware, making cross-sensor generalization difficult. We present FTP-1,the first generalist foundation tactile policy pretrained to acquire transferable tactile manipulation abilities across diverse sensors and embodiments. FTP-1 supports varied tactile inputs, including image-, array-, and state-based signals, by using heterogeneous encoders to project them into unified morphology-aware latent tokens that are jointly modeled by a shared tactile Transformer expert. Pretrained on a…

3. Très proche Pas pertinent

   TLA: Tactile-Language-Action Model for Contact-Rich Manipulation

   Peng Hao, Chao-Fan Zhang, Ding-Zhe Li et al. · 2025 · Robot Learning · Pas encore résumé

   Résumé scientifique

   Significant progress has been made in vision-language models. However, language-conditioned robotic manipulation for contact-rich tasks remains underexplored, particularly in terms of tactile sensing. To address this gap, we introduce the Tactile-Language-Action (TLA) model, which effectively processes sequential tactile feedback via cross-modal language grounding to enable robust policy generation in contact-intensive scenarios. In addition, we construct a comprehensive dataset that contains 24k pairs of tactile action instruction data, customized for fingertip peg-in-hole assembly, providing essential resources for TLA training and evaluation. Our results show that TLA significantly outper…

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   Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding

   Zheng-Tong Xu, Yeping Wang, Ben Abbatematteo et al. · 2026 · Robotics · Pas encore résumé

   Résumé scientifique

   Contact-rich dexterous manipulation with multi-finger hands remains an open challenge in robotics because task success depends on multi-point contacts that continuously evolve and are highly sensitive to object geometry, frictional transitions, and slip. Recently, tactile-informed manipulation policies have shown promise. However, most use tactile signals as additional observations rather than modeling contact state or how their action outputs interact with low-level controller dynamics. We present Contact-Grounded Policy (CGP), a visuotactile policy that grounds multi-point contacts by predicting coupled trajectories of actual robot state and tactile feedback, and using a learned contact-co…

5. Très proche Pas pertinent

   Has GPT-6-Astra Achieved Embodied Intelligence?

   SenseNova, Joint Research Team, : et al. · 2026 · Robotics · Résumé IA disponible

   En bref

   Le rapport évalue GPT-6-Astra en robotique et conclut qu’il atteint une compétence incarnée notable, sans démontrer encore une intelligence incarnée pratique.

6. Très proche Pas pertinent

   Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation

   Ruilin Chen, Jing-Kai Jia, Tong Yang et al. · 2026 · arXiv.org · Pas encore résumé

   Résumé scientifique

   Tactile-enhanced vision-language-action (VLA) policies have been introduced for contact-rich manipulation, where critical interaction states are often hidden from vision. Future tactile prediction is a promising way to use touch because it turns tactile outcomes into supervision for action-induced contact dynamics. Yet VLA policies contain representations with different roles, from perceptual encoding to motor prediction, making it unclear where this supervision should be applied. We study this as a representation-alignment problem. Through a linear probe analysis, we find that future tactile states are most predictable from intermediate action-expert features, rather than from vision-langua…

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   Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation

   Han Xue, Jie-Ji Ren, Wen-Di Chen et al. · 2025 · Robotics · Pas encore résumé

   Résumé scientifique

   Humans can accomplish complex contact-rich tasks using vision and touch, with highly reactive capabilities such as fast response to external changes and adaptive control of contact forces; however, this remains challenging for robots. Existing visual imitation learning (IL) approaches rely on action chunking to model complex behaviors, which lacks the ability to respond instantly to real-time tactile feedback during the chunk execution. Furthermore, most teleoperation systems struggle to provide fine-grained tactile / force feedback, which limits the range of tasks that can be performed. To address these challenges, we introduce TactAR, a low-cost teleoperation system that provides real-time…

8. Très proche Pas pertinent

   TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance

   Zhemeng Zhang, Jia-Hua Ma, Xin-Cheng Yang et al. · 2026 · Robotics · Pas encore résumé

   Résumé scientifique

   Fine-grained and contact-rich manipulation remain challenging for robots, largely due to the underutilization of tactile feedback. To address this, we introduce TouchGuide, a novel cross-policy visuo-tactile fusion paradigm that fuses modalities within a low-dimensional action space. Specifically, TouchGuide operates in two stages to guide a pre-trained diffusion or flow-matching visuomotor policy at inference time. First, the policy produces a coarse, visually-plausible action using only visual inputs during early sampling. Second, a task-specific Contact Physical Model (CPM) provides tactile guidance to steer and refine the action, ensuring it aligns with realistic physical contact conditi…

Les travaux antérieurs sur lesquels il s’appuie

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   UniVTAC: A Unified Simulation Platform for Visuo-Tactile Manipulation Data Generation, Learning, and Benchmarking

   Bai-Jun Chen, Wei-Jie Wan, Tian-Xing Chen et al. · 2026 · arXiv.org · Pas encore résumé

   Résumé scientifique

   Robotic manipulation has seen rapid progress with vision-language-action (VLA) policies. However, visuo-tactile perception is critical for contact-rich manipulation, as tasks such as insertion are difficult to complete robustly using vision alone. At the same time, acquiring large-scale and reliable tactile data in the physical world remains costly and challenging, and the lack of a unified evaluation platform further limits policy learning and systematic analysis. To address these challenges, we propose UniVTAC, a simulation-based visuo-tactile data synthesis platform that supports three commonly used visuo-tactile sensors and enables scalable and controllable generation of informative cont…

2. Proche Pas pertinent

   VLA-Touch: Enhancing Vision-Language-Action Model With Dual-Level Tactile Feedback

   Jian-Xin Bi, K. Ma, Ce Hao et al. · 2026 · IEEE Robotics and Automation Letters · Pas encore résumé

   Résumé scientifique

   Tactile feedback is generally recognized to be crucial for effective interaction with the physical world. However, state-of-the-art Vision-Language-Action (VLA) models often lack the ability to interpret and use tactile signals, limiting their effectiveness in contact-rich tasks. Incorporating tactile feedback into these systems is challenging due to the absence of large multi-modal datasets. We present VLA-Touch, an approach that enhances generalist robot policies with tactile sensing without fine-tuning the base VLA with tactile data. Our method introduces two key innovations: (1) a pipeline that leverages a pretrained tactile-language model to provide semantic tactile feedback for high-le…

3. Proche Pas pertinent

   VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policies

   Abraham George, Selam Gano, Pranav Katragadda et al. · 2024 · IEEE International Conference on Robotics and Automation · Pas encore résumé

   Résumé scientifique

   Tactile information is a critical tool for dexterous manipulation. As humans, we rely heavily on tactile information to understand objects in our environments and how to interact with them. We use touch not only to perform manipulation tasks but also to learn how to perform these tasks. Therefore, to create robotic agents that can learn to complete manipulation tasks at a human or super-human level of performance, we need to properly incorporate tactile information into both skill execution and skill learning. In this paper, we investigate how we can incorporate tactile information into imitation learning platforms to improve performance on manipulation tasks. We show that incorporating visu…

4. Proche Pas pertinent

   OmniVTLA: Vision-Tactile-Language-Action Models With Semantic-Aligned Tactile Sensing

   Zheng-Xue Cheng, Yi-Qian Zhang, Anni Tang et al. · 2025 · IEEE Robotics and Automation Letters · Pas encore résumé

   Résumé scientifique

   Recent vision-language-action (VLA) models build upon vision-language foundation models, and have achieved promising results and exhibit the potential of task generalization in robot manipulation. However, due to the difficulty of acquiring tactile data, current VLA models significantly overlook the importance of tactile perception and fail in contact-rich tasks. To address this issue, this paper proposes OmniVTLA, a novel framework that models vision, tactile, and language for end-to-end contact-rich manipulation tasks. Specifically, we introduce ObjTac, a comprehensive tactile dataset comprising textual, visual, and tactile information for 56 objects across 10 categories. Leveraging its 13…

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   N0-VTLA: Scaling Vision-Tactile-Language-Action Model with Latent Tactile Tokens

   NeoteAI Team, Fudan Teai Team · 2026 · arXiv.org · Pas encore résumé

   Résumé scientifique

   We present $N_0$-VTLA, a vision-tactile-language-action (VTLA) foundation model capable of (1) fine-grained contact-rich manipulation with tactile perception and tactile-feedback control, and (2) offline policy improvement from stored deployment data. Building on current vision-based backbones, we propose a training recipe for tactile integration consisting of visuo-tactile pre-training, staged tactile-pathway integration, and advantage-conditioned offline policy improvement. During pre-training, the policy learns broad contact priors from NeoData, our large-scale visuo-tactile robot dataset; to our knowledge, $N_0$-VTLA is the first VTLA model pretrained on tactile data at scale. During pos…