A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation

By Lei Tai, Jingwei Zhang, Joschka Boedecker, Wolfram Burgard. 2016

82 citations recorded by the source corpus.

Deep learning techniques have been widely applied, achieving state-of-the-art results in various fields of study. This survey focuses on deep learning solutions that target learning control policies for robotics applications. We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning. For deep reinforcement learning (DRL), we begin from traditional reinforcement learning algorithms, showing how they are extended to the deep context and effective mechanisms that could be added on top of the DRL algorithms. We then introduce representative works that utilize DRL to solve navigation and manipulation tasks in robotics. We continue our discussion on methods addressing the challenge of the reality gap for transferring DRL policies trained in simulation to real-world scenarios, and summarize robotics simulation platforms for conducting DRL research. For imitation leaning, we go through its three main categories, behavior cloning, inverse reinforcement learning and generative adversarial imitation learning, by introducing their formulations and their corresponding robotics applications. Finally, we discuss the open challenges and research frontiers.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Reinforcement Learning, Robotics & Control

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lesia Semenova
2. Chudi Zhong
3. Zhi Chen
4. Haiyang Huang
5. Cynthia Rudin
6. Tong Wang
7. Octavio Loyola-González
8. Berk Ustun

In Computer Vision and Pattern Recognition

1. Zhi Chen
2. Lesia Semenova
3. Chudi Zhong
4. Haiyang Huang
5. Cynthia Rudin

In Control and Systems Engineering

1. Lesia Semenova
2. Cynthia Rudin

In Reinforcement Learning

1. Zhi Chen
2. Cynthia Rudin

In Robotics & Control

1. Cynthia Rudin

Citations in the corpus

• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “Recently, deep reinforcement learning algorithms have achieved state-of-the-art performance in mastering the game of Go and various Atari games [273, 210] and have been actively used in robotic control [156, 283], simulated autonomous navigation and self-driving cars [150, 265], dialogue summarizati”