Tactile sensing can improve performance in contact-rich robot manipulation. However, learning policies that use real tactile feedback typically requires demonstrations collected with tactile-equipped hardware. We present Feature-Extracted Latent Tactile (FELT), a framework that uses existing paired visuo-tactile data to learn contact-aware representations from RGB observations. FELT provides these representations to downstream policies through two interfaces: generated tactile images and contact-aware features. FELT combines a frozen visual encoder with separate query decoders for the left and right finger, reflecting the spatial organization of dual-finger tactile arrays. The two branches exchange information through gated connections, and a convolutional readout produces spatial contact representations for the downstream interfaces. For downstream manipulation, the image interface decodes these spatial representations into tactile images, allowing policies trained with either real or generated tactile observations to use generated images at deployment. Alternatively, the feature interface feeds these representations directly to the policy during both training and deployment, without real tactile observations. Experiments on four contact-rich real-robot tasks show that both interfaces achieve higher success rates than vision-only policies. Directly incorporating learned contact-aware features into the policy improves success by 15-22.5 percentage points across the four tasks.

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