ActGaze: 反事実的視覚介入による行動に基づく注視学習で高精度マニピュレーションを実現
ActGaze: Learning Action-Grounded Gaze through Counterfactual Visual Interventions for High-Precision Manipulation
VLAモデルの視覚的注意をタスクに関連する領域に集中させるため、反事実的視覚介入によって行動予測に重要な領域を特定し、注視を学習させる手法を提案。実機実験で高精度マニピュレーションタスクにおいて既存手法を上回る性能を示した。
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著者: Jinxuan Zhu, Jiaheng Wang, Chao Tang, Mengfan Wang, Hao Wei, Shengbao Li, Hong Yin, Yiwen Gao, Chenrui Tie, Tingguang Li
分類: cs.RO
原文アブストラクト
Current Vision-Language-Action (VLA) models often struggle with high-precision robotic manipulation. We attribute this limitation primarily to their visual attention being dispersed across task-irrelevant regions. To address this issue, we propose ActGaze, a training approach that guides VLA policies to gaze on task-relevant regions, much like humans gaze on critical visual cues while executing precise movements. Unlike prior methods that rely on external labels for gaze supervision, ActGaze derives spatial supervision directly from the VLA's own action objective by using counterfactual visual interventions to identify regions that are critical for action prediction. Extensive real-robot experiments on four high-precision robotic manipulation tasks demonstrate that ActGaze induces more focused visual attention on task-relevant regions and consistently outperforms the base VLA policy and other visual-grounding approaches.