Highly robust running of articulated bipeds in unobserved terrain

Abstract

Control design of running robots is often based on mapping the behavior of lower order models onto the robotic systems, and the robustness of running is largely determined by the robustness of these underlying models. However, existing implementations do not take full advantage of the stability that the low order models can provide. In particular, analysis of the theoretical spring mass model suggests leg placement policies that generate near deadbeat rejection of large, unobserved changes in ground height. Here we show in simulation that this blind robustness to rough terrain can be carried over to bipedal robots. We design a control that stably embeds the spring mass model's behavior in a planar robot model and show that resulting system rejects ground disturbances of up to 25% leg length, adapts to persistent ground slopes, and tolerates sensor noise, signal delays, and modeling errors. The results indicate that transferring control derived within the spring mass model is an effective technique for realizing highly robust running in robotic systems.