SOURCE-LINKED INTELLIGENCE
Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis
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 kinematic mapping and force-closure-based stability estimation, while offloading task-dependent understanding to specialized foundation-model modules. These modules provide composable priors that are
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T17:03:11.000Z
First collected: 2026-09-21T04:31:57.454Z. This is not the publication date.