Computational Modeling and Deep Learning for Industrial Robotics

Abstract

By incorporating deep learning techniques, industrial robots are a core pillar of Industry 4.0, driving intelligent automation, adaptive control, predictive maintenance, and improved manufacturing efficiency. In this review, we look into the state-of-the-art developments of deep learning in the field of industrial robotic systems with regards to fault diagnosis, object recognition, robot control, inverse kinematics and dynamics, digital twins, collaborative robotics, energy optimization, robotic grasping, weld seam detection, and cybersecurity. The review methodology comprised a systematic review of the literature that involved qualitative synthesis and examination of 24 peer-reviewed research articles and conference papers between 2014 and 2025 from the top journals and conference proceedings. Selected studies were classified based on application domain, deep learning architecture, performance goals and implementation challenges to analyze the trends and advances in research and technology. The analysis has shown that convolutional neural networks, long short-term memory networks, deep reinforcement learning, hybrid deep learning models, and digital twin frameworks are able to better enhance the perception capabilities, motion planning, fault prediction, positioning accuracy, operational safety, and energy efficiency of robots. While these improvements have been made, industrial-scale adoption of these models still faces challenges in terms of computational complexity, real-time deployment, data availability, cybersecurity, model interpretability, and scalability. The review concludes that deep learning, in combination with Industrial Internet of Robotic Things (IIoRT), digital twins, edge intelligence, and explainable artificial intelligence (XAI), will pave the way for the next generation of autonomous, reliable, secure, and sustainable industrial robotic systems in smart manufacturing environments.