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arXiv · 2607.21049

GuidedAttention: Interpretable and Correctable Visual Attention for OOD-Robust Robot Manipulation via Imitation Learning

Masaki Murooka·Ryoichi Nakajo·Keisuke Shirai·Tomohiro Motoda·Hanbit Oh·Ryo Hanai·Yukiyasu Domae
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Abstract

End-to-end visuomotor policies provide little opportunity for humans to understand or correct the policy's visual attention. We propose GuidedAttention, a visuomotor imitation learning framework that introduces interpretable and correctable visual attention as an explicit intermediate representation. Task-relevant attention keypoints are predicted from camera images and condition a diffusion-based action policy. Users can inspect and optionally correct selected keypoints once at rollout initialization, after which the corrected attention is automatically propagated throughout execution by a tracking module. Experiments in simulation and the real world demonstrate that GuidedAttention consistently improves robot manipulation performance, particularly under positional and appearance out-of-distribution (OOD) conditions. https://mmurooka.github.io/guided-attention-project-page

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Masaki Murooka, Ryoichi Nakajo, Keisuke Shirai, Tomohiro Motoda, Hanbit Oh, Ryo Hanai, Yukiyasu Domae. 2026-07-23. GuidedAttention: Interpretable and Correctable Visual Attention for OOD-Robust Robot Manipulation via Imitation Learning. https://arxiv.org/abs/2607.21049

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