Multilayer Perceptrons for Bio-inspired Friction Estimation
Few years old children lift and manipulate unfamiliar objects more dexterously than today’s robots. Therefore, it has arisen an interest at the artificial intelligence community to look for inspiration on neurophysiological studies to design better models for the robots. The estimation of the friction coefficient of the object’s material is a crucial information in a human dexterous manipulation. Humans estimate the friction coefficient based on the responses of their tactile mechanoreceptors. In this paper, finite element analysis was used to model a finger and an object. Simulated human afferent responses were then obtained for different friction coefficients. Multiple multilayer perceptrons that received as input simulated human afferent responses, and gave as output an estimation of the friction coefficient, were trained and tested. A performance analysis was carried out to verify the influence of the following factors: number of hidden neurons, compression ratio of the input pattern, partitions of the input pattern.
KeywordsHide Layer Compression Ratio Spike Train Hide Neuron Grip Force
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