A Review of Deep Learning-Based Industrial Robot Grasping Systems

Ziyang Yan

doi:10.47297/taposatWSP2633-456930.20250608

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A Review of Deep Learning-Based Industrial Robot Grasping Systems

6卷第8期

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A Review of Deep Learning-Based Industrial Robot Grasping Systems
A Review of Deep Learning-Based Industrial Robot Grasping Systems
School of Mechanical Engineering, Hefei University of Technology,Mechanical Design, Manufacturing, and Automation,Hefei, Anhui, 230001, China
2025年6卷第8期页码：164-167
纸质出版：2025-08-30
DOI：10.47297/taposatWSP2633-456930.20250608
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(2025). A Review of Deep Learning-Based Industrial Robot Grasping Systems. 科技理论与实践（英文版）, 6(8), 164-167.

Yan Ziyang.A Review of Deep Learning-Based Industrial Robot Grasping Systems[J].Theory and Practice of Science and Technology,2025,06(08):164-167.
(2025). A Review of Deep Learning-Based Industrial Robot Grasping Systems. 科技理论与实践（英文版）, 6(8), 164-167. DOI： 10.47297/taposatWSP2633-456930.20250608.

Yan Ziyang.A Review of Deep Learning-Based Industrial Robot Grasping Systems[J].Theory and Practice of Science and Technology,2025,06(08):164-167. DOI： 10.47297/taposatWSP2633-456930.20250608.

摘要

Abstract

Traditional industrial robot grasping methods rely on predefined rules and structured environments

making them ill-suited for complex scenarios and dynamic changes. With breakthroughs in deep learning across visual perception and reinforcement learning

robotic grasping technology is progressively shifting toward a data-driven intelligent paradigm. This paper focuses on two key technologies: visual perception and grasping strategy optimization. In visual perception

grasp detection and 6D pose estimation based on convolutional neural networks significantly enhance system robustness in complex environments. In strategy optimization

imitation learning and deep reinforcement learning offer novel pathways for autonomous decision-making and skill acquisition in dynamic environments. However

current systems still face challenges including data scarcity

simulation-reality gaps

and insufficient multimodal perception fusion. Future research should focus on few-shot learning

transfer techniques from simulation to reality

multimodal information fusion

and non-rigid object grasping to advance the development of general-purpose intelligent grasping systems.

关键词

Keywords

references

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