Reinforcement Learning for Orientation on the Lie Algebra

Naseem Alhousani, Hatice Kose, Fares J. Abu-Dakka

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

Abstract

In this paper, we propose a novel framework for Reinforcement Learning on Lie algebra and show how it applies to learning the orientation of the robot's end effector in the task space. The proposed framework is suitable for model-free Reinforcement learning algorithms. Our research is motivated by the fact that in robotics, non-Euclidean data (e.g., orientation) is common in learning manipulation skills, yet neglecting the geometric meaning of such data affects learning performance and accuracy. In particular, our innovation is to apply policy parameterization and learning on the Lie algebra, then map back the learned actions to the hemisphere manifold. The proposed framework opens the door for some model-free Reinforcement learning algorithms designed for Euclidean space to learn non-Euclidean data without change. According to the best of our knowledge, this research work is the first effort in applying a policy parameterization in the context of Reinforcement learning on the Lie algebra of the hemisphere manifold. The results of our experiments provide evidence to support our hypothesis that learning orientation on the Lie algebra is more precise and leads to a superior solution than learning through the normalization of non-Euclidean data.

Translated title of the contributionOryantasyon Öǧrenimi için Lie Cebiri Üzerinde Takviyeli Öǧrenme
Original languageEnglish
Title of host publication31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350343557
DOIs
Publication statusPublished - 2023
Event31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 - Istanbul, Turkey
Duration: 5 Jul 20238 Jul 2023

Publication series

Name31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Conference

Conference31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Country/TerritoryTurkey
CityIstanbul
Period5/07/238/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Lie algebra
  • policy optimization
  • policy search
  • re-inforcement learning

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