University of Hertfordshire

By the same authors

Using a SOFM to learn Object Affordances

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    Final published version, 9.23 MB, PDF document

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Original languageEnglish
Title of host publicationProcs 5th Workshop of Physical Agents (WAF'04)
PublisherUniversity of Edinburgh
Publication statusPublished - 2004

Abstract

Learning affordances can be defined as learning action potentials, i.e., learning that an object exhibiting certain regularities offers the possibility of performing a particular action. We propose a method to endow an agent with the capability of acquiring this knowledge by relating the object invariants with the potentiality of performing an action via interaction episodes with each object. We introduce a biologically inspired model to test this learning hypothesis and a set of experiments to check its validity in a Webots simulator with a Khepera robot in a simple environment. The experiment set aims to show the use of a GWR network to cluster the sensory input of the agent; furthermore, that the aforementioned algorithm for neural clustering can be used as a--starting point to build agents that learn the relevant functional bindings between the cues in the environment and the internal needs of an agent.

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