Abstract
We detail an approach to the autonomous acquisition of hierarchical perception-action competences in which capabilities are bootstrapped using an information-based saliency measure.
Our principle aim is hence to accelerate learning in embodied autonomous agents by aggregating novel motor capabilities and their corresponding perceptual representations using a subsumption-based strategy. The method seeks to allocate affordance parameterizations according to the current (possibly autonomously-determined) learning goal in a manner that eliminates redundant percept-motor context, thereby obtaining maximal parametric efficiency.
Experimental results within a simulated environment indicate that doing so reduces the complexity of a multistage perception-action learning problem by several orders of magnitude.
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Windridge, D., Shevchenko, M., Kittler, J. (2008). An Entropy-Based Approach to the Hierarchical Acquisition of Perception-Action Capabilities. In: Caputo, B., Vincze, M. (eds) Cognitive Vision. ICVW 2008. Lecture Notes in Computer Science, vol 5329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92781-5_7
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DOI: https://doi.org/10.1007/978-3-540-92781-5_7
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