How 30,000 Hours of Tactile Data Give Embodied AI a Human‑like Sense of Touch

The article presents a 30,000‑hour multimodal tactile‑visual dataset, unified representation models, and two downstream systems (VTLA and TWAM) that demonstrate how large‑scale touch data can dramatically improve robot manipulation success rates and establish tactile perception as a core scaling law for embodied intelligence.

embodied AImultimodal learningrobot manipulation

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