Human Based Cost from Persistent Homology for Bipedal Walking

Abstract While the focus of robotic bipedal walking to date has been the development of anthropomorphic gait, the community has been unable to agree on a model for such gait. In this paper, we propose a universal ordering of events for bipedal walking based on motion capture data collected from a walking experiment. We process the motion capture data using persistent homology to automatically determine the ordering of discrete events. Surprisingly, every subject in the experiment had an identical ordering of such events. This universal ordering allows us to propose a cost function based upon human data: the human-based cost.