Deep Learning-Driven Perception and Control for Intelligent Robotic Systems

Abstract

With the rapid development of artificial intelligence, deep learning has become a core enabling technology for intelligent robotic systems. Traditional robotic perception and control methods often rely on handcrafted features and rule-based strategies, which struggle to adapt to complex and dynamic environments. This paper proposes a deep learning-driven integrated framework for robotic perception, decision-making, and motion control. By combining convolutional neural networks and reinforcement learning, the proposed system achieves robust environmental understanding and adaptive control. Extensive experiments on real-world robotic platforms demonstrate that the proposed approach significantly improves navigation accuracy, task success rate, and operational stability compared with conventional methods.