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DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination

Paper recorded by Signals 4 on 2026-09-08 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-08 on arXiv · recorded by Signals 4 on 2026-09-09

Category: cs.AI · 人工智能 · first seen 2026-09-09

Abstract

Dexterous manipulation involves contact-rich and fine-grained interactions with the physical world, posing significant challenges for existing vision-language-action (VLA) models due to severe visual occlusions and complex contact dynamics. While recent works have incorporated tactile sensing into robotic manipulation, most approaches still rely on homogeneous multimodal fusion, lacking adaptive t

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#149 most recent of 300 cs.AI papers we have recorded · ↑ newer: Canonical Color as a Lens into Concept Decodability in Vision Encoders · ↓ older: MeClear: Cooperative Game-Theoretic Attribution and Risk-Aware Memory
Cite this page: DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination: the #149 most recent of 300 cs.AI papers we have recorded (as of 2026-09-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/decal-towards-physically-grounded-dexterous-vision-language-action-models-via-co.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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