Deep Learning for Autonomous Robotics: A Review of Perception and Decision-Making

Abstract

 In recent years, deep learning has revolutionized autonomous robotics by enabling advanced perception and decision-making capabilities. This review paper focuses on the state-of-the-art deep learning methods in autonomous robotics, particularly in the areas of perception and decision-making. We discuss the significant progress made in vision-based perception and reinforcement learning for control. However, challenges such as real-time processing under resource constraints and sample inefficiency in reinforcement learning remain. Additionally, we explore the advancements in human-robot interaction and the ethical concerns associated with autonomous decision-making. We also identify gaps in sim-to-real transfer and multi-agent collaboration. By reviewing the major results and discussions in these areas, this paper aims to provide a comprehensive overview of the current research landscape and highlight important directions for future research. The insights gained from this review could potentially bridge the gap between theoretical research and practical implementation, offering valuable guidance for researchers and practitioners in advancing the field of autonomous robotics. Collectively, these efforts pave the way for more capable, reliable, and human-centric autonomous systems that can serve diverse applications in the real world.