Movement Prediction from Real-World Images Using a Liquid State Machine
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.
KeywordsOutput Neuron Recurrent Neural Network Movement Prediction Liquid Pool Supervise Learning Algorithm
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- 1.Jordan, M.I., Wolpert, D.M.: Computational motor control. In: Gazzaniga, M. (ed.) The Cognitive Neurosciences. MIT Press, Cambridge (1999)Google Scholar
- 2.Jaeger, H.: The echo state approach to analysing and training recurrent neural networks. Technical Report 148, GMD (2001)Google Scholar
- 5.Bear, M.F.: Neuroscience: Exploring the brain. Williams and Wilkins, Baltimore (2000)Google Scholar
- 7.Thomson, A.M., West, D.C., Wang, Y., Bannister, A.P.: Synaptic connections and small circuits involving excitatory and inhibitory neurons in layers 2-5 of adult rat and cat neocortex: Triple intracellular recordings and biocytin labelling in vitro. Cerebral Cortex 12(9), 936–953 (2002)CrossRefGoogle Scholar