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.
Unit: Unified tactile representation for robot learning
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ViTacFormer learns a cross-modal visuo-tactile latent space with autoregressive tactile prediction and an easy-to-hard curriculum, then uses the representation for imitation learning that yields ~50% higher success and the first reported 11-stage, 2.5-minute autonomous dexterous tasks.
TacImag framework trains on paired visuotactile data to predict tactile observations from vision, improving performance on six simulated and four real-world manipulation tasks.
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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ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation
ViTacFormer learns a cross-modal visuo-tactile latent space with autoregressive tactile prediction and an easy-to-hard curriculum, then uses the representation for imitation learning that yields ~50% higher success and the first reported 11-stage, 2.5-minute autonomous dexterous tasks.
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Imagining the Sense of Touch: Touch-Informed Manipulation via Imagined Tactile Representations
TacImag framework trains on paired visuotactile data to predict tactile observations from vision, improving performance on six simulated and four real-world manipulation tasks.