Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation

• URL: http://arxiv.org/abs/2607.14609v1
• Date: Thu, 16 Jul 2026 06:12:05 GMT
• Title: Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation
• Abstract summary: Future prediction is a promising way to use touch because it turns tactile into supervision for action-induced contact dynamics.<n>We study this as a representation-alignment problem.<n>Motivated by this observation, we introduce a lightweight Latent Tactile Predictor.
• Score: 33.650798630141594
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Tactile-enhanced vision-language-action (VLA) policies have been introduced for contact-rich manipulation, where critical interaction states are often hidden from vision. Future tactile prediction is a promising way to use touch because it turns tactile outcomes into supervision for action-induced contact dynamics. Yet VLA policies contain representations with different roles, from perceptual encoding to motor prediction, making it unclear where this supervision should be applied. We study this as a representation-alignment problem. Through a linear probe analysis, we find that future tactile states are most predictable from intermediate action-expert features, rather than from vision-language features or final action states. Motivated by this observation, we introduce a lightweight Latent Tactile Predictor (LTP), which predicts compact future tactile embeddings from the identified intermediate representation. By avoiding direct prediction of noisy raw tactile signals, LTP provides an action-outcome grounding signal that aligns intermediate action representations with future contact consequences. Experiments on real-world contact-rich manipulation tasks show that representation-aligned tactile grounding outperforms less aligned or multi-interface tactile prediction, highlighting the importance of where tactile supervision is applied.

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