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Robot Learning

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Abstract

With increasingly complex robot hardware and the push to enable robots to work in unstructured environments, there has been an increasing interest in applying machine learning and artificial intelligence techniques to systems and control problems in robotics. At the same time, many machine learning approaches have been developed with robotic problems as motivating use cases. Robot learning spans the problems of learning models, sensing, acting, as well as integrated approaches and has been applied to many types of robot embodiments. The algorithms cover most fields of machine learning. However, due to the specific challenges in robotics, these either need to be adapted or new approaches have to be developed.

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Author information

Authors and Affiliations

  1. Cognitive Robotics department, Delft University of Technology, Delft, The Netherlands

    Jens Kober

Authors
  1. Jens Kober
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Correspondence to Jens Kober .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, Boston University, Boston, MA, USA

    John Baillieul

  2. Automation and Control Solutions, Honeywell, Golden Valley, MN, USA

    Tariq Samad

Section Editor information

  1. Dipartimento di Ingegneria Elettrica e Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, Napoli, Italy

    Bruno Siciliano

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Kober, J. (2020). Robot Learning. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_100027-1

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  • DOI: https://doi.org/10.1007/978-1-4471-5102-9_100027-1

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5102-9

  • Online ISBN: 978-1-4471-5102-9

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

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