Quantifying and Optimizing Robustness of Bipedal Walking Gaits on Rough Terrain

Legged robots need “good” disturbance rejection to operate reliably in real-world environments, and achieving this goal arguably requires quantifying robustness. In this work, we consider a point-foot biped on variable-height terrain and measure robustness by the expected number of steps before failure. Unlike our previous work, in which we always assumed a fixed set of low-level gait controllers exist and focused on high-level control design, in this work we finally use quantification of robustness to benchmark and optimize a given (low-level) controller itself. Specifically, we study two particular control strategies as case demonstrations. One scheme is the now-familiar hybrid zero dynamics approach and the other is a method using piece-wise reference trajectories with a sliding mode control. This work provides a methodology for optimization of a broad variety of parameterizable gait control strategies and illustrates dramatic increases in robustness due to both gait optimization and choice of control strategy.