TactX: Learning Shared Tactile Representations Across Diverse Sensors
For robotic manipulation, this work addresses the sensor-specificity bottleneck by enabling cross-sensor transfer of tactile policies, though the improvement is incremental over vision-only baselines.
TactX learns a shared tactile representation across resistive, magnetic, and vision-based sensors, enabling zero-shot policy transfer between physically distinct sensors. It improves average success rate on contact-rich manipulation tasks from 27.5% (vision-only) to 45.9%.
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.