Abstract

The rapid advancement of autonomous systems has underscored the critical role of deep learning in enhancing their capabilities. This book chapter provides a comprehensive examination of how deep learning techniques are applied to improve various aspects of autonomous systems, focusing on transfer learning and its impact on performance. Key areas explored include the application of simulated data for training, domain-invariant representation learning, and the fine-tuning of robot policies to adapt to new environments. Emphasis was placed on the utilization of domain-adversarial neural networks, self-supervised learning, and multi-modal data integration to bridge gaps between source and target domains. The chapter also addresses challenges related to data scarcity, computational efficiency, and model robustness, offering insights into future research directions. Through detailed case studies and theoretical analysis, this chapter aims to provide a clear understanding of how advanced deep learning techniques drive innovation in autonomous systems, highlighting their practical implications and future potential.

Introduction

The evolution of autonomous systems has been profoundly influenced by advancements in deep learning, which have significantly expanded the capabilities and potential applications of these technologies [1,2]. Autonomous systems, encompassing a range of applications from self-driving cars to robotic assistants, require sophisticated algorithms to process and interpret complex data [3]. Deep learning techniques, which involve training models using vast amounts of data and multiple layers of neural networks, have become instrumental in enhancing the functionality and adaptability of these systems [4]. This chapter provides an in-depth exploration of how deep learning techniques are applied to improve autonomous systems, focusing on transfer learning and its impact on performance across various domains [5].

Transfer learning has emerged as a critical strategy in addressing the challenges associated with training autonomous systems [6]. By leveraging pre-existing knowledge from related tasks or domains, transfer learning enables models to adapt to new environments with limited additional data [7]. This approach was particularly valuable in scenarios where collecting large amounts of labeled data was impractical or cost-prohibitive [8]. The chapter examines various methodologies within transfer learning, including the use of simulated data to enhance training processes, which allows for the generation of extensive and diverse datasets that are essential for robust system development [9,10].