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Vector Quantization Algorithm for Time Series Prediction and Visuo-Motor Control of Robots

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Verteilte Künstliche Intelligenz und kooperatives Arbeiten

Part of the book series: Informatik-Fachberichte ((2252,volume 291))

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Abstract

We describe a new algorithm for vector quantization and control. The algorithm, in addition to generating a discrete representation of input data by means of Voronoï polyhedra, also generates a tesselation associated with these polyhedra. The tesselation corresponds to a graph which connects neighbouring Voronoï polyhedra and, hence, reflects neighborhood relationships of the embedding space of the data. The algorithm can be extended to approximate through ‘training’ arbitrary functions defined on the data points. The tesselation allows one to speed up the ‘training’ through cooperative learning involving nearest, next-nearest, etc. Voronoï polyhedra, reducing the range of cooperation progressively during training. The algorithm produces a table look-up program, assigning optimally tables to inputs and generating rapidly optimal table entries. The entries can be complex data structures, e.g., combinations of scalars, vectors, and tensors. The use of the algorithm has been demonstrated for time series prediction, surpassing existing algorithms, and for visuo-motor control of an industrial robot, e.g., for precise end effector position control. We will attempt to demonstrate by the time of the lecture also an application of the algorithm for visuo-motor control of a pneumatically driven robot arm, a Bridgestone ‘RUBBERTUATOR’. This light-weight robot, capable of compliant motion, can be operated in contact with humans. The presented algorithm can acquire the complex response characteristics of this arm through training and, thereby, allows accurate and swift control of pneumatic robot motion.

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© 1991 Springer-Verlag Berlin Heidelberg

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Berkovitch, S. et al. (1991). Vector Quantization Algorithm for Time Series Prediction and Visuo-Motor Control of Robots. In: Brauer, W., Hernández, D. (eds) Verteilte Künstliche Intelligenz und kooperatives Arbeiten. Informatik-Fachberichte, vol 291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76980-1_41

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  • DOI: https://doi.org/10.1007/978-3-642-76980-1_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54617-7

  • Online ISBN: 978-3-642-76980-1

  • eBook Packages: Springer Book Archive

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