Transferring Human Manipulation Knowledge to Robots with Inverse Reinforcement Learning

Emil Blixt Hansen*, Rasmus Eckholdt Andersen, Steffen Madsen, Simon Bøgh

*Corresponding author

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Abstract

The need for adaptable models, e.g. reinforcement learning, have in recent years been more present within the industry. In this paper, we show how two versions of inverse reinforcement learning can be used to transfer task knowledge from a human expert to a robot in a dynamic environment. Moreover, a second method called Principal Component Analysis weighting is presented and discussed. The method shows potential in the use case but requires some more research.
Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020
Number of pages5
PublisherIEEE
Publication date2020
Pages933-937
Article number9025873
ISBN (Electronic)9781728166674
DOIs
Publication statusPublished - 2020
EventIEEE/SICE International Symposium on System Integration - Hawaii Convention Center, Honolulu, United States
Duration: 12 Jan 202015 Jan 2020

Conference

ConferenceIEEE/SICE International Symposium on System Integration
LocationHawaii Convention Center
CountryUnited States
CityHonolulu
Period12/01/202015/01/2020
SeriesProceedings of the 2020 IEEE/SICE International Symposium on System Integration
ISSN2474-2325

Keywords

  • Inverse Reinforcement Learning
  • Deep Reinforcement Learning
  • Robotics
  • Artificial Intelligence
  • Human-Robot interaction

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  • Projects

    MADE Digital WP5.3 - Designing Self-Configuring and Self-Learning Smart Factories

    Bøgh, S., Chrysostomou, D., Anguiozar, N. U., Andersen, R. S. & Arexolaleiba, N. A.

    01/03/201731/12/2019

    Project: Research

    Cite this

    Hansen, E. B., Andersen, R. E., Madsen, S., & Bøgh, S. (2020). Transferring Human Manipulation Knowledge to Robots with Inverse Reinforcement Learning. In Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020 (pp. 933-937). [9025873] IEEE. Proceedings of the 2020 IEEE/SICE International Symposium on System Integration https://doi.org/10.1109/SII46433.2020.9025873