Human-data based cost of bipedal robotic walking

This paper proposes a cost function constructed from human data, the human-based cost, which is used to gauge the "human-like" nature of robotic walking. This cost function is constructed by utilizing motion capture data from a 9 subject straight line walking experiment. Employing a novel technique to process the data, we determine the times when the number of contact points change during the course of a step which automatically determines the ordering of discrete events or the domain breakdown along with the amount of time spent in each domain. The result is a weighted graph or walking cycle, associated with each of the subjects walking gaits. Finding a weighted cycle that minimizes the cut distance between this collection of graphs produces an optimal or universal domain graph for walking together with an optimal walking cycle. In essence, we find a single domain graph and the time spent in each domain that yields the most "natural" and "human-like" bipedal walking. The human-based cost is then defined as the cut distance from this optimal gait. The main findings of this paper are two-fold: (1) when the human-based cost is computed for subjects in the experiment it detects medical conditions that result in aberrations in their walking, and (2) when the human-based cost is computed for existing robotic models the more human-like walking gaits are correctly identified.