Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review

TL;DR

This paper reviews recent advances in deep reinforcement learning algorithms tailored for robotic manipulation, highlighting progress, challenges, and the potential for real-world application despite ongoing issues with robustness and versatility.

Contribution

It provides a focused overview of recent deep reinforcement learning methods addressing robotic manipulation control, emphasizing progress in sample efficiency and generalization.

Findings

Significant progress in deep reinforcement learning algorithms for robotics

Persistent challenges in robustness and versatility for real-world use

Ongoing research aims to overcome sample efficiency and generalization issues

Abstract

Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence. Another subfield of machine learning named reinforcement learning, tries to find an optimal behavior strategy through interactions with the environment. Combining deep learning and reinforcement learning permits resolving critical issues relative to the dimensionality and scalability of data in tasks with sparse reward signals, such as robotic manipulation and control tasks, that neither method permits resolving when applied on its own. In this paper, we present recent significant progress of deep reinforcement learning algorithms, which try to tackle the problems for the application in the domain of robotic manipulation control, such as sample efficiency and generalization. Despite these continuous improvements, currently, the challenges of learning robust and versatile manipulation skills for robots with deep reinforcement learning are still far from being resolved for real world applications.