Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (3): 662-669.doi: 10.13229/j.cnki.jdxbgxb.20240739

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Autonomous grasping algorithm of robotic arm based on deep reinforcement learning

Cheng-jun TIAN(),Yu YAN,Ren-wei CUI,Jin-tong ZHANG   

  1. School of Electronic Information Engineering，Changchun University of Science and Technology，Changchun 130012，China
  • Received:2024-07-04 Online:2026-03-01 Published:2026-03-31

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Abstract:

Aiming at the problems such as the lack of independent learning ability of traditional robotic arms and poor adaptability in unknown environments， this paper proposes a deep reinforcement learning algorithm combining prior knowledge， and modifies the design of reward function and experience pool to solve the problems such as low data quality， slow convergence speed and unsatisfactory learning effect in the early stage of training， while enhancing its generalization. The results on Pybullet simulation platform show that compared with the original algorithm， the convergence speed of the improved model is increased by 48.5%， and the success rate is increased by 8%. Compared with other mainstream algorithms， the convergence speed and success rate are significantly improved.

Key words: pattern recognition and intelligent system, deep reinforcement learning, priori knowledge, robot arm

CLC Number: 

  • TP183

Fig.1

Model design with prior knowledge"

Fig.2

Flow chart of multi-experience pool"

Fig.3

Six-degree-of-freedom robotic arm"

Fig.4

Three-finger flexible gripper"

Fig.5

Layout and training of the environment in Pybullet"

Fig.6

Comparison of reward values between original and improved algorithms"

Fig.7

Comparison of the success rate of the original algorithm and the improved algorithm"

Fig.8

Comparison of the convergence of the improved algorithm and the original algorithm"

Fig.9

Comparison of the success rate of the improved algorithm and the original algorithm"

Fig.10

Comparison of the convergence of the improved algorithm with other algorithms"

Fig.11

Comparison of the success rate of the improved algorithm with other algorithms"

Table 1

Multi-object grasping test"

实验物体每100次成功次数识别IOU
螺丝刀920.90
订书器990.94
香蕉800.88
锤子880.88
牙膏960.92
柠檬820.80
网球920.82
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