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Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis

Paper recorded by Signals 4 on 2026-09-03 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-03 on arXiv · recorded by Signals 4 on 2026-09-04

Category: cs.CV · 计算机视觉 · first seen 2026-09-04

Abstract

This paper proposes AdaRoboVLG, a task-adaptive Vision-Language-Grasp (VLG) framework that supports generalizable grasp synthesis across different robotic hands. Unlike existing VLG methods that tightly couple foundation models with end-to-end grasp policies, AdaRoboVLG learns an efficient generalizable base policy that generates and evaluates physically feasible grasp candidates through explicit

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#152 most recent of 237 cs.CV papers we have recorded · ↑ newer: The Shape of Time: Video-Token Contrast for Temporal Understanding in · ↓ older: Efficient Semantic Understanding from Digital Foveation
Cite this page: Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis: the #152 most recent of 237 cs.CV papers we have recorded (as of 2026-09-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/adaptive-vision-language-grasping-via-composable-foundation-priors-and-generaliz.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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