  Robots are used for collecting samples from natural environments to create
models of, for example, temperature or algae fields in the ocean. Adaptive
informative sampling is a proven technique for this kind of spatial field
modeling. This paper compares the performance of humans versus adaptive
informative sampling algorithms for selecting informative waypoints. The humans
and simulated robot are given the same information for selecting waypoints, and
both are evaluated on the accuracy of the resulting model. We developed a
graphical user interface for selecting waypoints and visualizing samples.
Eleven participants iteratively picked waypoints for twelve scenarios. Our
simulated robot used Gaussian Process regression with two entropy-based
optimization criteria to iteratively choose waypoints. Our results show that
the robot can on average perform better than the average human, and
approximately as good as the best human, when the model assumptions correspond
to the actual field. However, when the model assumptions do not correspond as
well to the characteristics of the field, both human and robot performance are
no better than random sampling.
