A perceptual memory system for affordance learning in humanoid robots

Authors:
Marc Kammer;Marko Tscherepanow;Thomas Schack;Yukie Nagai
Affiliations:
Cognitive Interaction Technology, Center of Excellence and Applied Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany;Cognitive Interaction Technology, Center of Excellence and Applied Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany;Cognitive Interaction Technology, Center of Excellence and Neurocognition and Action, Faculty of Psychology and Sport Sciences, Bielefeld University, Bielefeld, Germany;Cognitive Interaction Technology, Center of Excellence, Bielefeld University, Bielefeld, Germany and Graduate School of Engineering, Osaka University, Suita, Osaka, Japan
Venue:
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Year:
2011

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Abstract

Memory constitutes an essential cognitive capability of humans and animals. It allows them to act in very complex, non-stationary environments. In this paper, we propose a perceptual memory system, which is intended to be applied on a humanoid robot learning affordances. According to the properties of biological memory systems, it has been designed in such a way as to enable life-long learning without catastrophic forgetting. Based on clustering sensory information, a symbolic representation is derived automatically. In contrast to alternative approaches, our memory system does not rely on pre-trained models and works completely unsupervised.