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:
- Spatial: DINOv3 features lifted to 3D + Contact-GraspNet; 88.4% avg success on cluttered DexGraspNet 2.0
- Cognitive: open-set affordance reasoning reaches 94%/87% accuracy (part/grasp type) with RAG+CoT; language-guided grasping: 81.3%/86.0% avg success on GraspClutter6D/GraspNet-1B across three hands
- Temporal: SAM3 mask tracking + cross-frame consistency updates grasp constraints online at 5Hz for moving targets
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.
More from Embodied
- NYU's Steertuning Lets a Robot Learn a Chore From 5 Minutes of Human Demo — CyberRobooo · 2026-11-11
- Boston Dynamics Atlas demoed with Blender 5.3 — TomLikesRobots · 2026-11-11
- Lamport Robotics posts robot 'wake-up test' demo video — R4nsack · 2026-11-11
- Embodied AI Faces Two-Order-of-Magnitude Data Gap as Industry Heads to 10M Hours — GaryMarcus · 2026-11-11
- NVIDIA hosts CoRL 2026 meetup on scaling robot training with Isaac Lab — m_wulfmeier · 2026-11-11
- Lighthouse extends Obsidian welding foundation model to run on any robotic platform — bennash · 2026-11-11