DIPOLE: 視覚と幾何を融合したロバストな視覚運動汎化
DIPOLE: Fusing Vision and Geometry for Robust Visuomotor Generalization
視覚と言語・幾何の相補的なモダリティを訓練時のドロップアウトとクロスアテンションで融合し、照明や視点変化に強い拡散ポリシーを実現した。
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著者: Yikai Tang, Haoran Geng, Jindou Jia, Yuxuan Hu, Sheng Zang, Jianfei Yang, Pieter Abbeel, Jitendra Malik
分類: cs.RO
原文アブストラクト
Imitation learning has emerged as a crucial approach for acquiring visuomotor skills from demonstrations, where designing effective observation encoders is essential for policy generalization. However, existing methods tend to struggle once test-time conditions differ from the demonstrations, such as changes in lighting, texture, viewpoint, object placement, or object identity. To address this challenge, we propose DIffusion POlicy with compLementarity Encoders (DIPOLE), a visuomotor policy that learns to fuse complementary modalities through a training-time mechanism rather than a specialized fusion architecture. A modality-wise dropout masks one branch at each training step, encouraging each modality to remain individually informative. A lightweight cross-attention layer then exchanges complementary cues between the two. This design endows DIPOLE with five core strengths: stable high performance across diverse tasks, robustness to visual changes, spatial generalization at sub-centimeter precision, emergent capability beyond either modality, and zero-shot transfer to unseen objects. Across 18 simulated and 4 real-world tasks, DIPOLE outperforms six baselines by 39.1% on average, with gains of 41.5% under unseen visual distractors and 15.2% under randomized object placement.
