RoboGrasp: A Universal Grasping Policy for Robust Robotic Control
Journal:
arXiv
Published Date:
Feb 5, 2025
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.
