Deep Learning in Robotics: A Review of Recent Research

2017/07/22 by Harry A. Pierson, Michael S. Gashler, Pierson, Harry A. +1 · 1 voice · 3 citations
Computer Science · Engineering · #Advanced Neural Network Applications #Anomaly Detection Techniques and Applications #Robot Manipulation and Learning #cs.RO

paper · pdf · doi:10.48550/arxiv.1707.07217

openalex publication_date 2017/07/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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-à-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.

Citations

• Kaldi Speech Recognition Toolkit
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Learning in Neural Networks: An Overview
• Gradient-based learning applied to document recognition
• A Novel Connectionist System for Unconstrained Handwriting Recognition
• Backpropagation Applied to Handwritten Zip Code Recognition
• ImageNet classification with deep convolutional neural networks
• Deep Exploration via Bootstrapped DQN
• Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

Cited by

• Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning
• Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret
• 3D Reconstruction via Incremental Structure From Motion

Discussions

• Deep Learning in Robotics: A Review of Recent Research [hn, 121 points, 16 comments]

Related

• A Survey of Deep Learning Techniques for Mobile Robot Applications
• The Limits and Potentials of Deep Learning for Robotics
• Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
• How to train your robot with deep reinforcement learning: lessons we have learned
• OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics