OFlow: 物体認識を組み込んだ時間的フローマッチングによる堅牢なロボット操作
OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation
ロボット操作のためのVLAモデルに、物体認識を組み込んだ時間的フローマッチングを導入し、将来予測と物体認識を統一した潜在空間で行うことで、分布シフト下での堅牢性を向上させた。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Kuanning Wang, Ke Fan, Chenhao Qiu, Zeyu Shangguan, Yuqian Fu, Yanwei Fu, Daniel Seita, Xiangyang Xue
分類: cs.RO
原文アブストラクト
Robust robotic manipulation requires not only predicting how the scene evolves over time, but also recognizing task-relevant objects in complex scenes. However, existing VLA models face two limitations. They typically act only on the current frame, while future prediction and object-aware reasoning are often learned in separate latent spaces. We propose OFlow (injecting Object-Aware Temporal Flow Matching into VLAs), a framework that addresses both limitations by unifying temporal foresight and object-aware reasoning in a shared semantic latent space. Our method forecasts future latents with temporal flow matching, factorizes them into object-aware representations that emphasize physically relevant cues while filtering task-irrelevant variation, and conditions continuous action generation on these predictions. By integrating OFlow into VLA pipelines, our method enables more reliable control under distribution shifts. Extensive experiments across LIBERO, LIBERO-Plus, MetaWorld, and SimplerEnv benchmarks and real-world tasks demonstrate that object-aware foresight consistently enhances robustness and success.
