The Role of Deep Learning in Autonomous Systems and Robotics

Abstract

When it comes to developing autonomous systems and robotics, deep learning has become an essential tool for making machines do complicated jobs with little to no human input. Deep learning techniques enable autonomous systems and robots to mimic human perception, understanding, and interaction with their surroundings by making use of massive datasets and sophisticated neural network topologies. deep learning's critical function in improving autonomous systems' capacities, particularly in areas like object identification, decision-making, and motion planning. the use of deep learning models like RNNs, CNNs, and reinforcement learning to a range of robotics applications, including autonomous driving, industrial automation, and healthcare robotics. Furthermore, the article delves into the difficulties linked to training DL models in ever-changing real-world contexts, covering topics such as computational power requirements, model generalisation, and the necessity for massive labelled datasets. Lastly, we will look at the future of deep learning in robotics, considering developments in sensor technology, real-time data processing, and the incorporation of AI into robotic systems to enhance machine intelligence, adaptability, and autonomy.