RoboGrasp: A Universal Grasping Policy for Robust Robotic Control

TL;DR

RoboGrasp introduces a universal grasping policy that combines pretrained detection models with robotic learning, significantly improving grasp success rates and generalizability across diverse scenarios.

Contribution

It presents a novel diffusion-based framework integrating visual guidance for robust, adaptable robotic grasping, addressing overfitting issues of previous methods.

Findings

Achieves up to 34% higher success rates in few-shot grasping tasks.

Enhances grasp stability and precision across diverse objects.

Demonstrates adaptability to various robotic learning paradigms.

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.