A robust stability approach to robot reinforcement learning based on a parameterization of stabilizing controllers

Stefan R. Friedrich, Martin Buss

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Reinforcement learning has become more and more popular in robotics for acquiring feedback controllers. Many approaches aim for learning a controller from scratch, i.e., data-driven without any modeling of the physical plant. However, stability properties of the closed loop are often not considered, or established only a-posteriori or ad hoc. We propose to employ reinforcement learning in the context of model-based control, allowing to learn in a framework of stabilizing controllers built by using only little prior model knowledge. This way, the action space is suitably structured for safe learning of a feedback controller to compensate for uncertainties due to model mismatch or external disturbances. The resulting scheme is developed around a decentralized PD feedback controller. Therefore, given such a controller, by the proposed method one can also add a learning module for performance enhancement. We demonstrate our approach both in simulation and in a hardware experiment using a two degree of freedom robot manipulator.

Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3365-3372
Number of pages8
ISBN (Electronic)9781509046331
DOIs
StatePublished - 21 Jul 2017
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: 29 May 20173 Jun 2017

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Country/TerritorySingapore
CitySingapore
Period29/05/173/06/17

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