G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation

2024-11-27

arXiv.org

AI Summary

This work addresses critical bottlenecks in 3D robotic imitation learning: the disconnection between geometric precision and semantic understanding, semantic degradation under occlusion, and reliance on manual annotations. We propose an “object-centric real-time 3D semantic flow” framework that pioneers a dynamic modeling approach integrating digital twin generation, vision foundation models (SAM/CLIP) for semantic extraction, and robust 6D pose tracking. Additionally, we introduce a diffusion-based strategy enhancement mechanism that is annotation-free, occlusion-resilient, and cross-object generalizable. Evaluated on five simulated dexterous manipulation tasks, our method achieves a terminal constraint success rate of 68.3% and cross-object generalization accuracy of 50.1%, substantially outperforming prior approaches. The framework establishes a novel paradigm toward human-level embodied dexterous manipulation.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• Enhances 3D robotic manipulation precision
• Integrates semantic understanding with geometric accuracy
• Improves cross-object generalization in robotics

Innovation

• Leverages foundation models
• Combines 3D generative models
• Integrates semantic flow updates