Ziyong Wang, Yupei Lin, Runxuan Huang, Liang Lin, Dongyu Zhang, Guangrun Wang · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28457 · open-access PDF
一般用途的多式模型已经证明了作为直接机器人行动政策的承诺,但它们是否掌握了现实世界交互的基础物理学,并能否应用这种理解来指导行动仍然未被探索。我们使用接触丰富的操纵作为代表性测试床来调查这个问题,将触觉理解视为这个更广泛的能力的具体例子。
Machine translation (Meta M2M100); the English original follows.
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
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