HUG learns robot grasping from 1 million human handshapes and beats baselines by 34%

LerrelPinto · x · 2026-07-29

The team behind HUG (Human Universal Grasping) flew their robot to Seattle for the Meta ARIA Summit to show that the system works in the real world.

• HUG is a flow-matching model that predicts diverse human-style grasps from a single RGB-D image.
• The training data, 1M-HUGs, contains 1 million egocentric grasp frames, 27.8 hours of video, 6,707 object instances, and 41 buildings.
• The model retargets grasps to different robot hands, enabling zero-shot grasping in everyday scenes.
• The authors also release a new benchmark, HUG-Bench, with 90 unseen objects, plus code, weights, dataset, and an interactive demo.
• On the real-world 30-object test set, HUG beats prior baselines by 23% and 34% on the challenging object set.

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