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Conference Paper

An Information-Theoretic On-Line Update Principle for Perception-Action Coupling

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Peng,  Z
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Peng, Z., Genewein, T., Leibfried, F., & Braun, D. (2017). An Information-Theoretic On-Line Update Principle for Perception-Action Coupling. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) (pp. 789-796). Piscataway, NJ, USA: IEEE.


Cite as: http://hdl.handle.net/21.11116/0000-0000-C387-B
Abstract
Inspired by findings of sensorimotor coupling in humans and animals, there has recently been a growing interest in the interaction between action and perception in robotic systems [1]. Here we consider perception and action as two serial information channels with limited information-processing capacity. We follow [2] and formulate a constrained optimization problem that maximizes utility under limited information-processing capacity in the two channels. As a solution we obtain an optimal perceptual channel and an optimal action channel that are coupled such that perceptual information is optimized with respect to downstream processing in the action module. The main novelty of this study is that we propose an online optimization procedure to find bounded-optimal perception and action channels in parameterized serial perception-action systems. In particular, we implement the perceptual channel as a multi-layer neural network and the action channel as a multinomial distribution. We illustrate our method in a NAO robot simulator with a simplified cup lifting task.