DEEP LEARNING TECHNOLOGIES IN ROBOTICS: APPLICATIONS, ADVANCES, AND FUTURE PERSPECTIVES
Keywords:
deep learning, robotics, convolutional neural networks, reinforcement learning, human-robot interaction, autonomous systems, artificial intelligenceAbstract
This paper presents a comprehensive investigation into the integration of Deep Learning (DL) technologies within robotic systems. Over the past decade, deep learning has fundamentally transformed robotics, enabling machines to perceive, reason, and act with unprecedented accuracy. We analyze six major DL architectures — Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs/LSTMs), Generative Adversarial Networks (GANs), Transformers, Reinforcement Learning (RL), and Graph Neural Networks (GNNs) — and their specific applications across healthcare, manufacturing, logistics, agriculture, autonomous vehicles, space exploration, and education. Empirical data from 2018–2024 demonstrates a compound annual growth rate (CAGR) of 23.3% in the AI robotics market, projected to reach USD 68 billion by 2028. Performance benchmarks show accuracy rates of 91–98% across diverse robotic tasks. The paper also identifies key challenges including data scarcity, computational costs, and ethical concerns, while proposing future research directions emphasizing edge AI, neuromorphic computing, and human-robot collaboration (HRC).
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