  The Spatial Sensitivity Function (SSF) is used to quantify a detector's
sensitivity to a spatially-distributed input signal. By weighting the incoming
signal with the SSF and integrating, the overall scalar response of the
detector can be estimated. This project focuses on estimating the SSF of a
light intensity sensor consisting of a photodiode. This light sensor has been
used previously in the Knuth Cyberphysics Laboratory on a robotic arm that
performs its own experiments to locate a white circle in a dark field (Knuth et
al., 2007). To use the light sensor to learn about its surroundings, the
robot's inference software must be able to model and predict the light sensor's
response to a hypothesized stimulus. Previous models of the light sensor
treated it as a point sensor and ignored its spatial characteristics. Here we
propose a parametric approach where the SSF is described by a mixture of
Gaussians (MOG). By performing controlled calibration experiments with known
stimulus inputs, we used nested sampling to estimate the SSF of the light
sensor using an MOG model with the number of Gaussians ranging from one to
five. By comparing the evidence computed for each MOG model, we found that one
Gaussian is sufficient to describe the SSF to the accuracy we require. Future
work will involve incorporating this more accurate SSF into the Bayesian
machine learning software for the robotic system and studying how this detailed
information about the properties of the light sensor will improve robot's
ability to learn.
