Movement prediction from real-world images using a liquid state machine

摘要

The prediction of time series is an important task in finance, economy, object tracking, state estimation and robotics. Prediction is in general either based on a well-known mathematical description of the system behind the time series or learned from previously collected time series. In this work we introduce a novel approach to learn predictions of real world time series like object trajectories in robotics. 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. The 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. The main advantages of this approach are that due to the nonlinear projection of the input data to a high-dimensional space simple learning algorithms can be used, that the liquid state machine provides temporal memory capabilities and that this kind of computation appears biologically more plausible than conventional methods for prediction. Our results support the idea that learning with a liquid state machine is a generic powerful tool for prediction.