Deep Learning Techniques for Enhancing Autonomous System Capabilities

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.