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A Hierarchical Autonomous Robot Controller for Learning and Memory: Adaptation in a Dynamic Environment

Department of System Design Engineering, University of Fukui, Japan

Indra Bin Mohd Zin

Department of Human and Artificial Intelligence Systems, Graduate School of Engineering, University of Fukui, Japan

Kazuyuki Murase

Department of System Design Engineering, University of Fukui, Japan, murase{at}synapse.his.fukui-u.ac.jp, Department of Human and Artificial Intelligence Systems, Graduate School of Engineering, University of Fukui, Japan, Research and Education Program for Life Science, University of Fukui, Japan

This work concerns practical issues surrounding the application of learning and memory in a real mobile robot with the goal of optimal navigation in dynamic environments. A novel hierarchical adaptive controller that contains two-level units was developed and trained in a physical mobile robot "e-Puck." In the low-level unit, the robot holds a number of biologically inspired Aplysia -like spiking neural networks that have the property of spike time-dependent plasticity. Each of these networks is trained to become an expert in a particular local environment(s). All the trained networks are stored in a tree-type memory structure that is located in the high-level unit. These stored networks are used as experiences for the robot to enhance its navigation ability in both new and previously trained environments. The robot's memory is designed to hold memories of various lengths and has a simple searching mechanism. Forgetting and dynamic clustering techniques are used to control the memory size. Experimental results show that the proposed model can produce a robot with learning and memorizing capabilities that enable it to survive in complex and highly dynamic environments.

Key Words: Aplysia-like spiking neural network • clustering • forgetting • learning • memory • spike time-dependent plasticity • tree-type memory

Adaptive Behavior, Vol. 17, No. 3, 179-196 (2009)
DOI: 10.1177/1059712309105814


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