GazeAct: A Human-Gaze-Annotated Manipulation Dataset for Attention-Regularized Policies
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摘自论文摘要
Vision-language-action (VLA) policies must ground language instructions in the right objects, contact points, and target locations for reliable manipulation. Human gaze provides a natural signal for this grounding, but existing robot datasets rarely pair real human gaze with the multi-camera observations used by modern policies.
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