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Learning on Real Robots from Their Direct Interaction with the Environment

  • Conference paper
Advances in Autonomous Robotics (TAROS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7429))

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Abstract

We present a new solution to achieve fast and continuous learning and adaptation processes on a real robot, even when the robot receives reinforcement from a human observer. The person does not need to have any kind of robotics knowledge, and will be able to provide the reward signal to the robot with a wireless joystick. Despite this highly-non-deterministic reinforcement, the robot is able to reach the desired behaviour in short periods of time.

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References

  1. Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. MIT Press (1998)

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  2. Thomaz, A., Breazeal, C.: Teachable robots: Understanding human teaching behaviour to build more effective robot learners. Artificial Intelligence 172, 716–737 (2008)

    Article  Google Scholar 

  3. Quintía, P., Iglesias, R., Regueiro, C., Rodriguez, M.: Simultaneous learning of perception and action in mobile robots. Robotics and Autonomous Systems 58, 1306–1315 (2010)

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Author information

Authors and Affiliations

  1. Information Technologies Research Centre (CITIUS), University of Santiago de Compostela, Spain

    P. Quintía, R. Iglesias & M. A. Rodríguez

  2. Department of Electronics and Systems, University of A Coruña, Spain

    C. V. Regueiro

Authors
  1. P. Quintía
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  2. R. Iglesias
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  3. M. A. Rodríguez
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  4. C. V. Regueiro
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Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering and Bristol Robotics Laboratory, University of Bristol, Bristol, UK

    Guido Herrmann

  2. Intelligent Autonomous Systems Lab Faculty of Computing, Engineering & Mathematical Sciences, University of the West of England, BS16 1QY, Bristol, U.K.

    Matthew Studley

  3. University of the West of England, BS34 8QZ, Bristol, UK

    Martin Pearson , Andrew Conn  & Chris Melhuish ,  & 

  4. Department of Electrical and Electronic Engineering, Imperial College London, Exhibition Road, SW7 2BT, London, UK

    Mark Witkowski

  5. Robot Intelligence Technology Laboratory, Department of EECS, KAIST, Daejeon, Korea

    Jong-Hwan Kim

  6. National University of Singapore, Singapore

    Prahlad Vadakkepat

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Quintía, P., Iglesias, R., Rodríguez, M.A., Regueiro, C.V. (2012). Learning on Real Robots from Their Direct Interaction with the Environment. In: Herrmann, G., et al. Advances in Autonomous Robotics. TAROS 2012. Lecture Notes in Computer Science(), vol 7429. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32527-4_51

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  • DOI: https://doi.org/10.1007/978-3-642-32527-4_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32526-7

  • Online ISBN: 978-3-642-32527-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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