GPT-6 Astra Is a Native Visuo-Tactile Policy for Contact-Rich Manipulation

原文：GPT-6 Astra Is a Native Visuo-Tactile Policy for Contact-Rich Manipulation

作者：Chen Yang, Linzhe Shi, Hang Zhang, Mu Xu, Jiansheng Fan, Chen Wang

来源：arXiv cs.RO（机器人）

正文

Computer Science > Robotics

arXiv:2610.26846v1 (cs)

[Submitted on 19 Oct 2026]

Title:GPT-6 Astra Is a Native Visuo-Tactile Policy for Contact-Rich Manipulation

Authors:Chen Yang, Linzhe Shi, Hang Zhang, Mu Xu, Jiansheng Fan, Chen Wang

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Abstract:Recent advances in multimodal models have enabled GPT-6 Astra to perform robotic manipulation through in-context learning. Yet contact-rich control and tactile perception are often regarded as strengths of specialist policies, leaving open whether Astra can directly understand and use tactile observations. We provide, to our knowledge, the first demonstration that Astra directly interprets vision-based tactile images and uses their contact cues for manipulation. Tactile RGB images enter through its existing visual interface, enabling few-shot closed-loop control on nineteen simulated UniVTAC and OpenViTac tasks with 210 sampled scene seeds per setting. With visuo-tactile input, Astra achieves mean success rates of 93.8% and 81.8%, exceeding the strongest reported specialist baselines in our comparison by 22.9 and 13.1 percentage points, respectively. Vision-only performance also leads these baselines, reaching 76.3% and 80.6%. Trajectory analysis reveals active probing, cautious grasping, contact-guided alignment, and recovery, alongside difficulties with tangential loading and corrective execution. Our findings challenge the exclusivity of specialist policies in contact-rich manipulation and show that fine-grained vision can partially compensate for missing touch. They also identify vision-based tactile sensing as an interface for scaling contact understanding with foundation models and reveal signs of benchmark saturation. These results motivate joint visual-tactile pretraining, harder contact-rich evaluations, and physical-robot validation.

Comments:

22 pages, 19 figures, 5 tables. Project page: this https URL

Subjects:

Robotics (cs.RO)

Cite as:

arXiv:2610.26846 [cs.RO]

(or

arXiv:2610.26846v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.26846

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机器人

由「前沿雷达」于 2026-10-20 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。