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The iCub Platform: A Tool for Studying Intrinsically Motivated Learning

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Intrinsically Motivated Learning in Natural and Artificial Systems

Abstract

Intrinsically motivated robots are machines designed to operate for long periods of time, performing tasks for which they have not been programmed. These robots make extensive use of explorative, often unstructured actions in search for opportunities to learn and extract information from the environment. Research in this field faces challenges that need advances not only on the algorithms but also on the experimental platforms. The iCub is a humanoid platform that was designed to support research in cognitive systems. We review in this chapter the chief characteristics of the iCub robot, devoting particular attention to those aspects that make the platform particularly suitable to the study of intrinsically motivated learning. We provide details on the software architecture, the mechanical design, and the sensory system. We report examples of experiments and software modules to show how the robot can be programmed to obtain complex behaviors involving the interaction with the environment. The goal of this chapter is to illustrate the potential impact of the iCub on the scientific community at large and, in particular, on the field of intrinsically motivated learning.

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Notes

  1. 1.

    The RobotCub project was funded by the European Commission, Project IST-004370, under Strategic Objective 2.3.2.4: Cognitive Systems.

  2. 2.

    The iCub software and hardware are licensed under the GNU General Public License (GPL) and GNU Free Documentation License (FDL), respectively.

  3. 3.

    In position control, potentially large forces are produced to achieve a desired position. This is dangerous when unexpected interaction with the environment occurs because the robot is learning, exploring, or interacting with humans. In this scenario, as explained in Sect. 4.1, force control is a preferable approach.

  4. 4.

    Point Grey Research, Inc.: http://www.ptgray.com.

  5. 5.

    Xsens 3D Motion Tracking: http://www.xsens.com.

  6. 6.

    ATI Industrial Automation: http://www.ati-ia.com.

  7. 7.

    Open Computer Vision Library: http://sourceforge.net/projects/opencvlibrary.

  8. 8.

    Open Robot Control Software: http://www.orocos.org.

  9. 9.

    Robot Operating System: http://www.ros.org.

  10. 10.

    Kitware, Cross Platform Make: http://www.cmake.org.

  11. 11.

    Simplified Wrapper and Interface Generator: http://www.swig.org.

  12. 12.

    The ADAPTIVE Communication Environment: http://www.cs.wustl.edu/\schmidt/ACE.htm.

  13. 13.

    Open Dynamics Engine: http://www.ode.org.

  14. 14.

    iCub software documentation: http://www.icub.org

  15. 15.

    An impedance controller drives the arm by simulating virtual springs attached between the arm and a desired equilibrium point.

  16. 16.

    graspDetector: see iCub Software Documentation, http://www.icub.org

  17. 17.

    iKinArmCtrlF: see iCub software documentation: http://www.icub.org

  18. 18.

    In doing so, we ensure that this constraint receives higher priority in the minimization (Pattacini et al. 2010). This part of the task is fulfilled with a precision up to the value of the constant ε that is selected to be practically negligible (in our case 10 − 6 m).

  19. 19.

    A complete list of available methods is available on the documentation page of the ICartesianControl interface in the YARP software documentation: http://www.yarp.it

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Acknowledgements

The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreements No 231500 (ROBOSKIN), No 214668 (ITALK), and No 215805 (CHRIS).

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Authors and Affiliations

  1. Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy

    Lorenzo Natale, Francesco Nori, Giorgio Metta, Matteo Fumagalli, Serena Ivaldi, Ugo Pattacini, Marco Randazzo, Alexander Schmitz & Giulio Sandini

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  1. Lorenzo Natale
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  2. Francesco Nori
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  3. Giorgio Metta
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  4. Matteo Fumagalli
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  9. Giulio Sandini
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Correspondence to Lorenzo Natale .

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  1. Istituto di Scienze e Tecnologie della Cognizione Consiglio Nazionale delle Ricerche, Rome, Italy

    Gianluca Baldassarre  & Marco Mirolli  & 

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Natale, L. et al. (2013). The iCub Platform: A Tool for Studying Intrinsically Motivated Learning. In: Baldassarre, G., Mirolli, M. (eds) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32375-1_17

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  • DOI: https://doi.org/10.1007/978-3-642-32375-1_17

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