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

Ziyong Wang, Yue Lin, Runxuan Huang, Liang Lin, Dongyu Zhang, Guangrun Wang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28457 · cited by 0 works (OpenAlex) · open-access PDF

Indonesian

Model multi-modal yang disesuaikan dengan tujuan umum telah menunjukkan janji sebagai kebijakan tindakan robot langsung, namun apakah mereka memahami fisika yang mendasari interaksi dunia nyata dan dapat menerapkan pemahaman itu untuk membimbing tindakan masih belum dipelajari. Kami menyelidiki pertanyaan ini dengan menggunakan manipulasi yang kaya kontak sebagai tempat pengujian representatif, mengeksplorasi pemahaman taktil sebagai contoh konkret dari kemampuan yang lebih luas ini. Manipulasi yang kaya kontak membutuhkan interpretasi tactile feedback dan referensi tactile untuk penilaian kontak dan tindakan korektif. Kebijakan taktil yang dipelajari saat ini memperoleh kemampuan ini melalui pra-latihan khusus dan optimalisasi per tugas, dengan biaya pengujian dan pengujian yang substansial. Kami menyajikan GPT-as-Tactile-Policy, yang meringkaskan demonstrasi ke dalam tugas bimbingan dan referensi taktil

Machine translation (Meta M2M100); the English original follows.

Abstract

General-purpose multimodal models have shown promise as direct robot action policies, yet whether they grasp the physics underlying real-world interactions and can apply that understanding to guide action remains underexplored. We investigate this question using contact-rich manipulation as a representative testbed, examining tactile understanding as a concrete instance of this broader capability. Contact-rich manipulation requires interpreting tactile feedback and adjusting actions after obstruction. Existing learned tactile policies acquire these capabilities through specialized pretraining and per-task optimization, with substantial data and training costs. We present GPT-as-Tactile-Policy, which summarizes expert demonstrations into task guidance and tactile references for contact assessment and corrective action. GPT-6-Astra combines this context with multimodal observations and interaction history to generate control commands and adjust actions through contact feedback. Without additional robotics-specific pretraining or per-task policy fine-tuning, our method achieves 93.8\% mean success across five UniVTAC tasks. Overall performance is comparable to state-of-the-art policies, with higher success on selected tasks. On Insert Hole, tactile feedback improves success from 31\% to 99\%. Execution analysis further demonstrates contact-guided probing and correction, supporting manipulation progress through explicit expert experience and online feedback.

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Topics: Task (project management), GRASP, Action (physics), Computer science, Human–computer interaction, Context (archaeology)

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