TactX learns a shared latent representation across three tactile sensor modalities via joint training on paired contacts, enabling zero-shot policy transfer and higher success on pick-and-place, insertion, wiping, and reorientation tasks.
Ward-Cherrier, N
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Tactile-WAM with TAAM improves mean success rate by 38.9% overall and 86% on contact-rich tasks on ManiFeel by using VideoClean mask and touch-aware bias to prevent tactile pollution in world action models.
citing papers explorer
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TactX: Learning Shared Tactile Representations Across Diverse Sensors
TactX learns a shared latent representation across three tactile sensor modalities via joint training on paired contacts, enabling zero-shot policy transfer and higher success on pick-and-place, insertion, wiping, and reorientation tasks.
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Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric Attention
Tactile-WAM with TAAM improves mean success rate by 38.9% overall and 86% on contact-rich tasks on ManiFeel by using VideoClean mask and touch-aware bias to prevent tactile pollution in world action models.