Bipedal Robotic Running with Partial Hybrid Zero Dynamics and Human-Inspired Optimization

This paper presents a method for achieving stable "human-like" running in simulation by using human-inspired control. Data from human running experiments are processed, analyzed and split into the two domains: stance phase and flight phase. By examining this data, we present a set of outputs, i.e., functions of the kinematics, which appear to represent human running; moreover, we show that this output data can be described by the time-solution to a linear spring-mass-damper-termed the canonical locomotion function. This observation motivates the construction of a human-inspired optimization that determines the parameters of the canonical locomotion function that provide the best fit of the human data while simultaneously enforcing partial hybrid zero dynamics, i.e., that the human outputs track the canonical locomotion functions even through impacts. The main result is a method for numerically solving this optimization problem that provably results in stable robotic running. Simulation results are presented that demonstrate the "human-like" robotic running obtained through this procedure.