Stanislav G. Berkovitch, Philippe Dalger, Ted Hesselroth, Thomas Martinetz,
Benoît Noël, Jörg A. Walter, and Klaus Schulten.
Vector quantization algorithm for time series prediction and
visuo-motor control of robots.
In W. Brauer and D. Hernandez, editors, Verteilte
Künstliche Intelligenz und kooperatives Arbeiten, volume 291 of
Informatikfachberichte, pp. 443-447. Springer, Berlin, 1991.
BERK91
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 Voronoi 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. Voronoi 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 optimally 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 times series prediction, surpassing existing algorithms, and 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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