Intrinsically Motivated Developmental Learning of Communication in Robotic Agents

  • Michael Timothy Sheldon

Student thesis: Doctoral ThesisDoctor of Philosophy


This thesis is concerned with the emergence of communication in artificial agents as an integrated part of a more general developmental progression. We demonstrate how early gestural communication can emerge out of sensorimotor exploration before moving on to linguistic communication. We then show how communicative abilities can feed back into more general motor learning.
We take a cumulative developmental approach, with two different robotic platforms undergoing a series of psychologically inspired developmental stages. These begin with the robot learning about its own body’s capabilities and limitations, then on to object interaction, the learning of proto-imperative pointing and early language learning. Finally this culminates in more complex object interaction in the form of learning to build stacks of objects with the linguistic capabilities developed earlier being used to help guide the robot’s learning.
This developmental progression is supported by a schema learning mechanism which constructs a hierarchy of competencies capable of dealing with problems of gradually increasing complexity. To allow for the learning of general concepts we introduce an algorithm for the generalisation of schemas from a small number of examples through parameterisation.
Throughout the robot’s development its actions are driven by an intrinsic motivation system designed to mimic the play-like behaviour seen in infants. We suggest a possible approach to intrinsic motivation in a schema learning system and demonstrate how this can lead to the rapid unsupervised learning of both specific experiences and general concepts.
Date of Award15 Apr 2013
Original languageEnglish
Awarding Institution
  • Aberystwyth University
SupervisorMark Lee (Supervisor) & James Alexander Law (Supervisor)

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