Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation

2026-07-16

AI Summary

It remains unclear at which representational level tactile supervision should be applied to enhance performance in contact-intensive manipulation within vision-language-action policies. Through linear probing analysis, this work identifies that intermediate action-expert features are best suited for predicting future tactile signals. Building on this insight, the authors propose a lightweight latent tactile prediction mechanism that leverages tactile signals as grounding supervision to align intermediate representations with anticipated contact outcomes, thereby circumventing the need to directly model noisy raw tactile data. Evaluated on real-world contact-intensive tasks, the method significantly outperforms existing non-aligned or multi-interface tactile prediction approaches and provides the first evidence of the critical role played by intermediate action representations in tactile grounding.

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.

Problem

• tactile grounding
• representation alignment
• contact-rich manipulation
• vision-language-action policies
• future tactile prediction

Innovation

• representation alignment
• tactile grounding
• latent tactile prediction
• contact-rich manipulation
• vision-language-action policy