Proceedings ArticleDOI
Actor-critic reinforcement learning for tracking control in robotics
Yudha Pane,Subramanya Nageshrao,Robert Babuska +2 more
- pp 5819-5826
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TLDR
Experimental results and evaluation of a compensation method which improves the tracking performance of a nominal feedback controller by means of reinforcement learning (RL) have shown that the proposed RL-based compensation method significantly improves the performance of the nominal feedback controllers.Abstract:
In this article we provide experimental results and evaluation of a compensation method which improves the tracking performance of a nominal feedback controller by means of reinforcement learning (RL). The compensator is based on the actor-critic scheme and it adds a correction signal to the nominal control input with the goal to improve the tracking performance using on-line learning. The algorithm has been evaluated on a 6 DOF industrial robot manipulator with the objective to accurately track different types of reference trajectories. An extensive experimental study has shown that the proposed RL-based compensation method significantly improves the performance of the nominal feedback controller.read more
Citations
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Optimal and Autonomous Control Using Reinforcement Learning: A Survey
TL;DR: Q-learning and the integral RL algorithm as core algorithms for discrete time (DT) and continuous-time (CT) systems, respectively are discussed, and a new direction of off-policy RL for both CT and DT systems is discussed.
Journal ArticleDOI
Reinforcement learning based compensation methods for robot manipulators
TL;DR: Two reinforcement learning (RL) based compensation methods are introduced that compensate for unmodeled aberrations in industrial robotic manipulators and show a considerable performance improvement when compared to PD, MPC, and ILC.
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On the guidance, navigation and control of in-orbit space robotic missions: A survey and prospective vision
TL;DR: Two families of emerging control schemes based upon reinforcement learning and geometric mechanics are introduced as promising research directions in the GNC of space robotic systems.
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A Machine Learning Approach for Collaborative Robot Smart Manufacturing Inspection for Quality Control Systems
TL;DR: An innovative approach that uses a collaborative robot to support the smart inspection and corrective actions for quality control systems in the manufacturing process, complemented by an intelligent system that learns and adapts its behavior according to the inspected parts.
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Reinforcement learning for adaptive optimal control of continuous-time linear periodic systems
TL;DR: Study of the infinite-horizon adaptive optimal control of continuous-time linear periodic (CTLP) systems, using reinforcement learning techniques, demonstrates the efficacy of the proposed learning-based adaptive optimal Control algorithm.
References
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Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
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TL;DR: Though beginning its third decade of active research, the field of ILC shows no sign of slowing down and includes many results and learning algorithms beyond the scope of this survey.
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Reinforcement learning in robotics: A survey
TL;DR: This article attempts to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots by highlighting both key challenges in robot reinforcement learning as well as notable successes.
Reinforcement Learning in Robotics: A Survey.
Jens Kober,Jan Peters +1 more
TL;DR: A survey of work in reinforcement learning for behavior generation in robots can be found in this article, where the authors highlight key challenges in robot reinforcement learning as well as notable successes and discuss the role of algorithms, representations and prior knowledge in achieving these successes.