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.