Robot Learning and Adaptation for Intelligent Behavior

Abstract

This work will offer an overview of the function that machine learning plays in the field of robotics. The relevance of machine learning in enabling robots to acquire knowledge via experience and adjust their behaviour in response to new situations is underlined. This allows robots to adapt to their surroundings and become more efficient. The article delves further into a number of different approaches to machine learning, such as reinforcement learning, imitation learning, and deep learning. These approaches are particularly well-suited for use in robots. Some of the more modern approaches, such as meta-learning, Bayesian optimisation, domain randomization, and adversarial training, are presented here. The final section of the paper discusses the topic of the future of robotics, focusing on the possibility that robots may become more powerful and capable in the future, eventually taking over jobs that are either too dangerous or too time-consuming for people to manage.