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Movement Prediction from Real-World Images Using a Liquid State Machine

  • Harald Burgsteiner
  • Mark Kröll
  • Alexander Leopold
  • Gerald Steinbauer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3533)

Abstract

Prediction is an important task in robot motor control where it is used to gain feedback for a controller. With such a self-generated feedback, which is available before sensor readings from an environment can be processed, a controller can be stabilized and thus the performance of a moving robot in a real-world environment is improved. So far, only experiments with artificially generated data have shown good results. In a sequence of experiments we evaluate whether a liquid state machine in combination with a supervised learning algorithm can be used to predict ball trajectories with input data coming from a video camera mounted on a robot participating in the RoboCup. This pre-processed video data is fed into a recurrent spiking neural network. Connections to some output neurons are trained by linear regression to predict the position of a ball in various time steps ahead. Our results support the idea that learning with a liquid state machine can be applied not only to designed data but also to real, noisy data.

Keywords

Output Neuron Recurrent Neural Network Movement Prediction Liquid Pool Supervise Learning Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Harald Burgsteiner
    • 1
  • Mark Kröll
    • 2
  • Alexander Leopold
    • 2
  • Gerald Steinbauer
    • 3
  1. 1.InfoMed/Health Care EngineeringGraz University of Applied SciencesGrazAustria
  2. 2.Institute for Theoretical Computer ScienceGraz University of TechnologyGrazAustria
  3. 3.Institute for Software TechnologyGraz University of TechnologyGrazAustria

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