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A Framework for Modelling Local Human-Robot Interactions Based on Unsupervised Learning

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Social Robotics (ICSR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9979))

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Abstract

This paper addresses the problem of teaching a robot interaction behaviors using the imitation learning paradigm. Particularly, the approach makes use of Gaussian Mixture Models (GMMs) to model the physical interaction of the robot and the person when the robot is teleoperated or guided by an expert. The learned models are integrated into a sample-based planner, an RRT*, at two levels: as a cost function in order to plan trajectories considering behavior constraints, and as configuration space sampling bias to discard samples with low cost according to the behaviors. The algorithm is successfully tested in the laboratory using an actual robot and real trajectories examples provided by an expert.

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Author information

Authors and Affiliations

  1. School of Engineering, Universidad Pablo de Olavide, Crta. Utrera km 1, Seville, Spain

    Rafael Ramón-Vigo, Noé Pérez-Higueras & Luis Merino

  2. Department of System Engineering and Automation, University of Seville, Camino de Los Descubrimientos, s/n, Seville, Spain

    Fernando Caballero

Authors
  1. Rafael Ramón-Vigo
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  2. Noé Pérez-Higueras
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  3. Fernando Caballero
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  4. Luis Merino
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Corresponding author

Correspondence to Rafael Ramón-Vigo .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, The University of Kansas, Lawrence, Indiana, USA

    Arvin Agah

  2. Department of Mechanical Engineering, Qatar University, Doha, Qatar

    John-John Cabibihan

  3. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA

    Ayanna M. Howard

  4. Department of Systems Engineering and Automation, University Carlos III de Madrid, madrid, Spain

    Miguel A. Salichs

  5. Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, Tennessee, USA

    Hongsheng He

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Ramón-Vigo, R., Pérez-Higueras, N., Caballero, F., Merino, L. (2016). A Framework for Modelling Local Human-Robot Interactions Based on Unsupervised Learning. In: Agah, A., Cabibihan, JJ., Howard, A., Salichs, M., He, H. (eds) Social Robotics. ICSR 2016. Lecture Notes in Computer Science(), vol 9979. Springer, Cham. https://doi.org/10.1007/978-3-319-47437-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-47437-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47436-6

  • Online ISBN: 978-3-319-47437-3

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