Deep Learning in Robotics: A Review of Recent Research

TL;DR

This paper reviews recent advances in applying deep learning to robotics, highlighting its benefits, limitations, and potential to inspire further research in robotic systems.

Contribution

It provides a comprehensive overview of recent research, summarizing key applications, benefits, and limitations of deep learning in robotics.

Findings

Deep learning has been increasingly applied to robotic systems since 2014.

Recent research demonstrates significant benefits in perception and control tasks.

Limitations include data requirements and real-world deployment challenges.

Abstract

Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least thirty papers published on the subject between 2014 and the present. This review discusses the applications, benefits, and limitations of deep learning vis-\`a-vis physical robotic systems, using contemporary research as exemplars. It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics.