TactX: Learning Shared Tactile Representations Across Diverse Sensors

2026-06-30

AI Summary

This work addresses the limited cross-platform transferability of existing tactile perception strategies, which are heavily dependent on specific sensor modalities. To overcome this, the study proposes a unified, sensor-agnostic tactile representation by constructing a shared latent space across three heterogeneous tactile modalities—resistive, magnetic, and vision-based. This is achieved through modality-specific encoders, pairwise contact alignment signals, and joint training. The resulting representation enables zero-shot transfer of tactile policies across sensor types. Evaluated on four contact-intensive manipulation tasks, the method significantly improves average success rates from 27.5% to 45.9%, demonstrating effective disentanglement and generalization of cross-modal tactile perception and manipulation.

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.

Problem

• tactile representation
• sensor transferability
• cross-modal learning
• contact-rich manipulation
• sensor-agnostic

Innovation

• tactile representation
• cross-modal learning
• sensor-agnostic manipulation
• zero-shot transfer
• shared latent space