Paper Infomation
Optimization of Robotic Arm Grasping Strategy Based on Deep Reinforcement Learning
Full Text(PDF, 30KB)
Author: Dongjun He
Abstract: In recent years, robotic arm grasping has become a pivotal task in the field of robotics, with applications spanning from industrial automation to healthcare. The optimization of grasping strategies plays a crucial role in enhancing the effectiveness, efficiency, and reliability of robotic systems. This paper presents a novel approach to optimizing robotic arm grasping strategies based on deep reinforcement learning (DRL). Through the utilization of advanced DRL algorithms, such as Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods, and Proximal Policy Optimization (PPO), the study aims to improve the performance of robotic arms in grasping objects with varying shapes, sizes, and environmental conditions. The paper provides a detailed analysis of the various deep reinforcement learning methods used for grasping strategy optimization, emphasizing the strengths and weaknesses of each algorithm. It also presents a comprehensive framework for training the DRL models, including simulation environment setup, the optimization process, and the evaluation metrics for grasping success. The results demonstrate that the proposed approach significantly enhances the accuracy and stability of the robotic arm in performing grasping tasks. The study further explores the challenges in training deep reinforcement learning models for real-time robotic applications and offers solutions for improving the efficiency and reliability of grasping strategies.
Keywords: Robotic Arm; Grasping Strategy; Deep Reinforcement Learning; Q-Learning; DQN; Policy Gradient; PPO; Optimization; Simulation; Robotics
References:
[1] Hiba S, Smail T, Rachid S, et al. Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping[J].Applied Sciences,2021,11(17):7917-7917.
[2] Shu Y, Xiong C, Chen C. A Study on Robotic Arm Target Recognition and Grasping Method Based on Deep Learning[J].International Journal of Pattern Recognition and Artificial Intelligence,2024,38(05):
[3] Hui G, Qi H, Zhe W. Optimization of Robotic Arm Grasping through Fractional-Order Deep Deterministic Policy Gradient Algorithm[J].Journal of Physics: Conference Series,2023,2637(1):
[4] Hui G, Qi H, Zhe W. Optimization of Robotic Arm Grasping through Fractional-Order Deep Deterministic Policy Gradient Algorithm[J].Journal of Physics: Conference Series,2023,2637(1):
[5] Li H, Zhu J. Research on Robotic Arm Grasping Algorithm Based on an Enhanced Edge Network[J].Frontiers in Computing and Intelligent Systems,2024,10(3):65-70.
[6] Zhou J, Zuo G, Li X, et al. Motion control strategy for robotic arm using deep cascaded feature-enhancement Bayesian broad learning system with motion constraints.[J].ISA transactions,2025,160268-278.
[7] Zhizhuo Z, Change Z. Simulation of Robotic Arm Grasping Control Based on Proximal Policy Optimization Algorithm[J].Journal of Physics: Conference Series,2022,2203(1):
[8] Hiba S, Smail T, Rachid S, et al. Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping[J].Applied Sciences,2021,11(17):7917-7917.