AdaRoboVLG: task-adaptive vision-language grasping framework hits 83.3% success across 510 real-world grasps

机器之心 · wechat · 2026-09-25

Researchers from HUST, Peking University and Keenon Robotics propose AdaRoboVLG, a task-adaptive vision-language grasping (VLG) framework that decouples 'how the task wants the object grasped' from 'how the gripper stably grasps it,' linking task understanding to grasp synthesis via a unified interface (CGR + grasp type).

Three composable priors:

Real-robot validation: 83.3% overall success over 510 grasps of 102 everyday objects; 89.7% under conveyor-belt disturbances; dual-arm sorting and transparent-object handling added by plugging in modules — no retraining of the base policy (trained on 4M simulated trials across DH3/Allegro/Inspire hands in Isaac Sim). Paper, code and simulation are open-sourced.

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