接触を伴うロボット操作のための表現整合型触覚グラウンディング
Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation
触覚強化型VLAポリシーにおいて、将来の触覚状態を予測する中間表現の位置を特定し、軽量な潜在触覚予測器(LTP)を導入して行動表現と接触結果を整合させる手法を提案した。実機の接触を伴う操作タスクで有効性を実証した。
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著者: Ruilin Chen, Jingkai Jia, Tong Yang, Xinyu Zhou, Qiao Sun, Jiangwei Zhong, Shizeng Zhang, Nuo Chen, Bailin He, Wei Li, Wenqiang Zhang
分類: cs.RO
原文アブストラクト
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.
