GazeAct: 人間の視線注釈付き操作データセットと注意正則化ポリシー
GazeAct: A Human-Gaze-Annotated Manipulation Dataset for Attention-Regularized Policies
ロボット操作のための視線注釈付き実機データセットを構築し、訓練時のみ視線を教師信号として視覚注意を正則化することで、推論時に視線なしで成功率を向上させる手法を提案した。
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著者: Anupam Pani, Wang Yuan, Qifan Wang, Yanchao Yang
分類: cs.RO, cs.AI
原文アブストラクト
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 transfer gaze from the operator-selected view to the remaining synchronized cameras, producing spatially consistent targets across the policy's visual inputs. We then use these gaze-derived targets to regularize language-conditioned visual attention during training while keeping inference gaze-free. Across 18 real-world tasks, our method improves average success from 48.1% to 58.6%, with gains up to 25 percentage points. Ablations show that gaze works best as soft supervision and that real human gaze provides stronger attention targets than generic attention-shaping priors. The gains transfer to LIBERO-Spatial (57.9% to 69.6%) and different VLA backbones.These results show that gaze collected in the robot observation space can provide effective training-time targets for improving visual grounding in VLA manipulation.