G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation
This work addresses the challenge of real-time dynamic semantic feature understanding for robotic manipulation policies, representing an incremental advancement by combining existing components like 3D generative models and vision foundation models.
The paper tackles the problem of achieving human-level dexterity in 3D robotic manipulation by integrating geometric precision and semantic understanding, resulting in G3Flow, which improves terminal-constrained manipulation and cross-object generalization with up to 68.3% and 50.1% average success rates in simulation tasks.
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.