ChronoFlow-Policy: 視覚運動ポリシー学習における過去・現在・未来の相互作用フローの統合
ChronoFlow-Policy: Unifying Past-Current-Future Interaction Flow in Visuomotor Policy Learning
物体とグリッパーの3Dキーポイントを用いて過去・現在・未来の相互作用ダイナミクスを統一的に表現するChronoFlowを導入し、これを拡散ベースの視覚運動ポリシーと共学習させることで、長期的・非マルコフ的操作タスクでの性能を向上させた。
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著者: Bokai Lin, Yifu Xu, Xinyu Zhan, Hongjie Fang, Jialin Tian, Fu-Cheng Zhang, Yong-Lu Li, Cewu Lu, Lixin Yang
分類: cs.RO
原文アブストラクト
Visual signals play a crucial role in policy learning by enabling models to capture object motion and interaction dynamics. Just as humans reason about actions using both past experience and anticipated outcomes, effective policies should integrate past interactions with future predictions. However, existing visuomotor policies typically model either historical context or future dynamics in isolation, lacking a unified temporal representation of interaction dynamics. In this work, we introduce ChronoFlow, a temporally unified representation that captures past, current, and future interaction dynamics through sparse 3D keypoints of both objects and the gripper. Based on this representation, we propose ChronoFlow-Policy, a diffusion-based visuomotor policy that jointly learns ChronoFlow and action sequences through a co-training objective. Experiments on 14 simulated tasks and 5 real-world manipulation tasks demonstrate that ChronoFlow-Policy consistently outperforms strong diffusion-policy baselines and improves robustness in long-horizon and non-Markovian manipulation scenarios. Our project page is available at https://the-kamisato-sii.github.io/ChronoFlow-Policy-project-page/.
