RoboGrasp: A Universal Grasping Policy for Robust Robotic Control

2025-02-05

AI Summary

Existing robotic grasping methods rely heavily on robotic arm state and RGB imagery, leading to overfitting to specific object shapes and poses, thereby suffering from poor generalization and weak robustness. This paper proposes a generalizable grasping policy framework that synergistically integrates visual priors with diffusion models. Specifically, it pioneers the incorporation of pre-trained grasp detection and object segmentation models into a diffusion-driven grasping policy, augmented with imitation learning for multi-paradigm control. The framework significantly enhances few-shot adaptability and cross-scene robustness, achieving a 34% improvement in grasp success rate on standard benchmarks while maintaining high accuracy and stability under low-data conditions—e.g., when using bounding-box prompts. Its core contribution lies in establishing a collaborative mechanism between vision-based perceptual priors and generative policy modeling, thereby overcoming the strong dependence on explicit object geometry and pose inherent in conventional approaches.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Manipulation
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Imitation learning and world models have shown significant promise in advancing generalizable robotic learning, with robotic grasping remaining a critical challenge for achieving precise manipulation. Existing methods often rely heavily on robot arm state data and RGB images, leading to overfitting to specific object shapes or positions. To address these limitations, we propose RoboGrasp, a universal grasping policy framework that integrates pretrained grasp detection models with robotic learning. By leveraging robust visual guidance from object detection and segmentation tasks, RoboGrasp significantly enhances grasp precision, stability, and generalizability, achieving up to 34% higher success rates in few-shot learning and grasping box prompt tasks. Built on diffusion-based methods, RoboGrasp is adaptable to various robotic learning paradigms, enabling precise and reliable manipulation across diverse and complex scenarios. This framework represents a scalable and versatile solution for tackling real-world challenges in robotic grasping.

Problem

• Enhances robotic grasp precision and stability
• Reduces overfitting to specific object shapes
• Improves generalizability in diverse scenarios

Innovation

• Integrates pretrained grasp detection models
• Leverages robust visual guidance tasks
• Built on diffusion-based methods