Dimensionality Reduction for Sensorimotor Learning in Mobile Robotics
Mobile robotic systems with a wide variety of sensors, actuators, and onboard high-speed processors are commercially and readily available. The information processing capabilities of these system presently lack the robustness and sophistication of biological systems. One challenge is that the high-dimensional input signals from the sensors need to be converted into a smaller number of perceptually relevant features. This dimensionality reduction can be performed on static signals such as a single image or on dynamic data such as a speech spectrogram. This proceedings discusses several different models for dimensionality reduction that differ only on the constraints on the variables and parameters of the models. In particular, nonnegativity constraints are shown to give rise to distributed yet sparse representations of both static and dynamic data.