A survey on large model-driven embodied grasping technologies

Abstract

Robotic grasping is a crucial step in enabling practical robotic applications, and many scholars in the robotics field have conducted long-term and in-depth studies in this area. In recent years, embodied intelligent systems driven by large models have demonstrated remarkable potential in robotic grasping tasks. By integrating multimodal perception, semantic understanding, reasoning ability, and complex decision-making capabilities, they have promoted the evolution of grasping technology from simple action execution toward generalization, adaptability, and robustness across diverse, dynamic, and unstructured environments. These advances have further highlighted the importance of combining data-driven intelligence with physical priors to enhance both efficiency and safety. This paper reviews and summarizes key technologies related to large model-driven embodied intelligent grasping, including intelligent perception and scene understanding, grasping task planning and action execution, process optimization, advances in simulation platforms and datasets, as well as current challenges, open questions, and future prospects. The aim is to provide a comprehensive and systematic overview of the field, offering deeper insights, establishing connections across subdomains, and facilitating sustained progress for researchers and practitioners worldwide.