(a) These three panels illustrate the robot’s machine learning system’s view of the playing field using the naïve light sensor model as the system progresses through the first three measurements. The previously obtained measurement locations used to obtain light sensor data are indicated by the black-and-white squares indicating the relative intensity with respect to the naïve light sensor model. The next selected measurement location is indicated by the green square. The blue circles represent the 50 hypothesized circles sampled from the posterior probability. The shaded background represents the entropy map, which indicates the measurement locations that promise to provide maximal information about the circle to be characterized. Note that the low entropy area surrounding the white square indicates that the region is probably inside the white circle (not shown) and that measurements made there will not be as informative as measurements made elsewhere. The entropy map in Figure 2 shows the same experiment at a later stage after seven measurements have been recorded. (b) These three panels illustrate the robot’s machine learning system’s view of the playing field using the more accurate SSF light sensor model. Note that the entropy map reveals the circle edges to be highly informative. This is because it helps to identify not only whether the sensor is inside the circle (as is accomplished using the naïve light sensor model on the left), but also the extent to which the sensor is on the edge of the circle.
