Neuromorphic computing for robotic systems: approaches to autonomous adaptation and Real-time control

Abstract

This work examines the integration of neuromorphic computing into robotic systems, specifically new approaches to autonomous adaptation and learning. Neuromorphic computing mimics the operation of neural networks in the human brain, enabling the creation of more energy-efficient and flexible robotic systems. The primary focus is on comparing traditional methods with cutting-edge neuromorphic approaches, as well as the practical aspects of their implementation to enhance the ability of robots to self-learn and adapt in dynamic conditions.