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arXiv · 2606.31236

TactX: Learning Shared Tactile Representations Across Diverse Sensors

Junsung Park·Sachin Bhadang·Carmelo Sferrazza·Sha Yi·Xiaolong Wang
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Abstract

Tactile sensors provide critical information for contact-rich manipulation, yet tactile representations and policies remain tightly coupled to each specific sensor, limiting transferability across robots and hardware platforms. We propose TactX, a framework for learning a transferable tactile representation across sensors spanning three fundamentally different transduction modalities: resistive, magnetic, and vision-based. TactX maps heterogeneous tactile observations into a shared latent space through modality-specific encoders trained on paired contact data. Such paired interactions provide a natural alignment signal across modalities, and the encoders are jointly trained across all sensor pairs, inducing a consistent latent space for all sensor types. Our experiments show that TactX aligns tactile representations across sensors while preserving object-level contact information, as evidenced by sensor-identity prediction and object classification in the learned latent space. We evaluate TactX on four contact-rich manipulation tasks: pick-and-place, plug insertion, board wiping, and object reorientation, and show that policies trained with one sensor transfer zero-shot to physically distinct sensors through the shared latent. This improves the average success rate from 27.5% for vision-only policy to 45.9%, providing a step toward sensor-agnostic tactile manipulation.

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Junsung Park, Sachin Bhadang, Carmelo Sferrazza, Sha Yi, Xiaolong Wang. 2026-06-30. TactX: Learning Shared Tactile Representations Across Diverse Sensors. https://arxiv.org/abs/2606.31236

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