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The paper introduces RoboGrasp, a universal grasping policy framework that combines pretrained grasp detection models with robotic learning to improve grasp precision and generalizability. By using visual guidance from object detection and segmentation, RoboGrasp reduces reliance on robot arm state data and RGB images, mitigating overfitting. The framework, built on diffusion-based methods, achieves up to 34% higher success rates in few-shot learning and grasping box prompt tasks, demonstrating improved robustness in diverse scenarios.
Achieve 34% higher grasping success rates by fusing pretrained vision with robotic learning, sidestepping reliance on robot state data.
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.
