An extremal fields approach for the analysis of human planning and control performance

In this paper we describe a study of the human pilot control behavior in a planar goal directed flight task. The experimental data was collected using a miniature helicopter in an indoor flight test facility. To provide insight into the human's control behavior we developed a technique to extract extremal fields from the family of collected trajectories. These fields describe the spatial distribution of the vehicle states and cost-to-go, including their statistical distribution, which provides information about the variability of the pilot's control behavior over the task domain. Once extracted we can compare these fields to the value functions obtained from the task's equivalent optimal control problem. The comparison of the humanextracted and the computed value function maps suggests that on average, the human acts similarity to an optimal control policy. The results also suggests that a simple mass-point model used for our analysis, and motivated by the hypothesis that the pilot acts as a dynamic inverse controller, is sufficient to explain the pilot's performance at the planning level. We use these results to develop hypotheses about human planning and control processes and discuss their biological plausibility based on control-theoretic interpretations. We plan to use the new insights from this framework to help design more capable and versatile algorithms for autonomous vehicle control, as well as help design man-machine interfaces that enable a more natural link with the operator's internal control and planning processes.