G3Flow：用于姿态感知和可泛化物体操作的三维语义流生成
G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation
AI总结：
G3Flow提出一种利用基础模型构建实时三维语义流的框架，结合生成模型、视觉模型和姿态跟踪，提升扩散策略在终端约束操作和跨物体泛化上的表现，实验成功率最高达68.3%和50.1%。
摘要：
近年来，基于扩散策略的三维机器人操作模仿学习取得了令人鼓舞的成果。然而，要实现人类级别的灵巧性，需要将几何精度与语义理解无缝集成。我们提出了G3Flow，一种新颖的框架，通过利用基础模型构建实时语义流，即一种动态的、以物体为中心的三维语义表示。我们的方法独特地结合了用于数字孪生创建的三维生成模型、用于语义特征提取的视觉基础模型以及用于连续语义流更新的鲁棒姿态跟踪。这种集成即使在遮挡情况下也能实现完整的语义理解，同时消除了手动标注的需求。通过将语义流融入扩散策略，我们展示了在终端约束操作和跨物体泛化方面的显著改进。在五个仿真任务上的大量实验表明，G3Flow始终优于现有方法，在终端约束操作和跨物体泛化任务上分别达到了高达68.3%和50.1%的平均成功率。我们的结果证明了G3Flow在增强机器人操作策略的实时动态语义特征理解方面的有效性。
英文摘要：
Recent advances in imitation learning for 3D robotic manipulation have shown promising results with diffusion-based policies. However, achieving human-level dexterity requires seamless integration of geometric precision and semantic understanding. We present G3Flow, a novel framework that constructs real-time semantic flow, a dynamic, object-centric 3D semantic representation by leveraging foundation models. Our approach uniquely combines 3D generative models for digital twin creation, vision foundation models for semantic feature extraction, and robust pose tracking for continuous semantic flow updates. This integration enables complete semantic understanding even under occlusions while eliminating manual annotation requirements. By incorporating semantic flow into diffusion policies, we demonstrate significant improvements in both terminal-constrained manipulation and cross-object generalization. Extensive experiments across five simulation tasks show that G3Flow consistently outperforms existing approaches, achieving up to 68.3% and 50.1% average success rates on terminal-constrained manipulation and cross-object generalization tasks respectively. Our results demonstrate the effectiveness of G3Flow in enhancing real-time dynamic semantic feature understanding for robotic manipulation policies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
