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.
Uniforce: A unified latent force model for robot manipulation with diverse tactile sensors
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
HTT learns shared representations across heterogeneous tactile sensors using a new paired dataset and pretraining objectives, enabling transfer to unseen sensors and tasks.
Tac-DINO constructs a large tactile dataset and Vis-Tac Holographic Matching Benchmark, then proposes Vision-Tactile Patch Alignment (VTPA) methods that outperform non-aligned baselines on local-to-global feature matching.
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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Heterogeneous Tactile Transformer
HTT learns shared representations across heterogeneous tactile sensors using a new paired dataset and pretraining objectives, enabling transfer to unseen sensors and tasks.