GazeAct: A Human-Gaze-Annotated Manipulation Dataset for Attention-Regularized Policies
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. We introduce GazeAct, a gaze-equipped real-robot manipulation dataset and training framework for multi-view VLA policies. Using screen-based teleoperation, we record gaze directly on the robot camera feed viewed by the operator, yielding 12000 demonstrations across 40 tasks and five domains with 300 Hz gaze synchronized with images, proprioception, and actions. We...
| Authors | Anupam Pani, Wang Yuan, Qifan Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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