Research on Deep Learning Algorithms and Applications in Robotic Arm Control

Machine learning is one of the significant computer science fields in the modern world. As a subset of machine learning, deep learning is also becoming increasingly important. This paper mainly focuses on the application of deep learning in robotic arm control through analyzing the performance of various algorithms in different tasks in the real world. First, the paper introduces characteristics of multiple neural network architectures, including convolutional neural networks (CNNs), feedforward neural nets (FNNs), and recurrent neural networks (RNNs). Then the paper introduces multiple algorithms related to reinforcement learning, such as proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), and deep Q-networks (DQN), providing their basic information and methods. The paper also describes the advantages of imitation learning. After a brief account of the theoretical part of the knowledge, the paper links theory to practice. The paper evaluated the performance of these deep learning methods in robotic arm tasks across three distinct application scenarios, providing important references for algorithm selection and performance optimization. In the end, the paper discusses the current shortcomings and disadvantages of deep learning algorithms and offers some suggestions for its future development.