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A Developmental Framework for Cumulative Learning Robots

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Computational and Robotic Models of the Hierarchical Organization of Behavior

Abstract

Developmental psychology is the study of human cognitive growth. However there exists a huge gap between the psychologist’s theories and knowledge of behaviour and our ability to implement developmental processes in autonomous agents. In this chapter we describe an approach towards developmental growth for robotics that utilises natural constraints in a general learning mechanism. The method, summarised as Lift-Constraint, Act, Saturate (LCAS), is described and illustrated with results from experiments. We discuss how this approach is grounded in the topics of sensory-motor abstraction, intrinsic motivation (as novelty), and staged learning, and our belief that robotics can learn much from infant psychology.

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Authors and Affiliations

  1. Department of Computer Science, Aberystwyth University, Aberystwyth, UK

    Mark Lee, James Law & Martin Hülse

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  1. Mark Lee
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  2. James Law
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Correspondence to Mark Lee .

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

    Gianluca Baldassarre

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

    Marco Mirolli

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Lee, M., Law, J., Hülse, M. (2013). A Developmental Framework for Cumulative Learning Robots. In: Baldassarre, G., Mirolli, M. (eds) Computational and Robotic Models of the Hierarchical Organization of Behavior. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39875-9_9

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