The iCub platform: A tool for studying intrinsically motivated learning

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

Research output: Chapter in Book/Report/Conference proceedingChapter

26 Citations (Scopus)

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 of 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 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.

Original languageEnglish
Title of host publicationIntrinsically Motivated Learning in Natural and Artificial Systems
PublisherSpringer-Verlag Berlin Heidelberg
Pages433-458
Number of pages26
Volume9783642323751
ISBN (Electronic)9783642323751
ISBN (Print)364232374X, 9783642323744
DOIs
Publication statusPublished - 2013 Nov 1

ASJC Scopus subject areas

  • Computer Science(all)

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    Natale, L., Nori, F., Metta, G., Fumagalli, M., Ivaldi, S., Pattacini, U., Randazzo, M., Schmitz, A., & Sandini, G. (2013). The iCub platform: A tool for studying intrinsically motivated learning. In Intrinsically Motivated Learning in Natural and Artificial Systems (Vol. 9783642323751, pp. 433-458). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-32375-1_17