A Comprehensive Review of Robotics Advancements Through Imitation Learning for Self-Learning Systems

Abstract

In recent years, robotics and artificial intelligence (AI) have witnessed significant growth, particularly in self-learning systems. This paper examines the remarkable progress made in this area, with a particular focus on the utilisation of imitation learning. Self-learning robotics systems have demonstrated the autonomous acquisition of new skills, making them highly adaptable and versatile. Imitation learning is a crucial technique that allows robots to gain knowledge from human demonstrations. This paradigm allows machines to learn and replicate human actions, thus enhancing the capabilities of self-learning robotic technology. The primary objective of this research was to investigate the potential of imitation learning and evaluate its impact on the advancement of self-learning robotics. This paper provides a comprehensive overview of self-learning robotic systems using imitation learning, examining the foundational concepts, essential methodologies, and various applications in this intriguing area. Furthermore, we highlight recent developments, discuss current trends, and outline potential research initiatives to guide the continued development of self-learning robotic systems using imitation learning. This review aims to contribute to the evolving landscape of autonomous robotics by consolidating knowledge, identifying challenges, and fostering further innovation in the pursuit of intelligent self-learning machines.