Running into a trap: Numerical design of task-optimal preflex behaviors for delayed disturbance responses.

Legged robots enjoy kilohertz control rates but are still making incremental gains towards becoming as nimble as animals. In contrast, bipedal animals are amazingly robust runners despite lagged state feedback from protracted neuromechanical delays. Based on evidence from biological experiments, we posit that much of disturbance rejection can be offloaded from feedback control and encoded into feed-forward pre-reflexive behaviors called preflexes. We present a framework for the offline numerical generation of preflex behaviors to optimally stabilize legged locomotion tasks in the presence of response delays. By coupling directly collocated trajectory optimizations, we optimize the preflexive motion of a simple bipedal running model to recover from uncertain terrain geometry using minimal actuator work. In simulation, the optimized preflex maneuver showed 30-77% economy improvements over a level-ground strategy when responding to terrain deviating just 2-4cm from the nominal condition. We claim this "preflex-and-replan" framework for designing efficient and robust gaits is amenable to a variety of robots and extensible to arbitrary locomotion tasks.