Abstract
Neuromorphic motion sensors using analog very large scale integrated technology are attractive for use on battery powered robots which require a low payload. Their features include low power consumption, continuous computation, light-weight, and robustness to different light and contrast conditions. Their outputs are not compatible with controllers that require precise measurements from their sensors. We describe a preliminary investigation into neural architectures that can translate information from these type of sensors into an output suitable for controlling the motor outputs of a robot. In this work, we use a neural network to produce an output that is similar to the range measurements of infrared range sensors, and we use this output to guide the behavior of the robot in a collision-avoidance task.
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