Don't break a leg: Running birds from quail to ostrich prioritise leg safety and economy in uneven terrain

Cursorial ground birds are paragons of bipedal running that span a 500-fold mass range from quail to ostrich. Here we investigate the task-level control priorities of cursorial birds by analysing how they negotiate single-step obstacles that create a conflict between body stability (attenuating deviations in body motion) and consistent leg force–length dynamics (for economy and leg safety). We also test the hypothesis that control priorities shift between body stability and leg safety with increasing body size, reflecting use of active control to overcome size-related challenges. Weight-support demands lead to a shift towards straighter legs and stiffer steady gait with increasing body size, but it remains unknown whether non-steady locomotor priorities diverge with size. We found that all measured species used a consistent obstacle negotiation strategy, involving unsteady body dynamics to minimise fluctuations in leg posture and loading across multiple steps, not directly prioritising body stability. Peak leg forces remained remarkably consistent across obstacle terrain, within 0.35 body weights of level running for obstacle heights from 0.1 to 0.5 times leg length. All species used similar stance leg actuation patterns, involving asymmetric force–length trajectories and posture-dependent actuation to add or remove energy depending on landing conditions. We present a simple stance leg model that explains key features of avian bipedal locomotion, and suggests economy as a key priority on both level and uneven terrain. We suggest that running ground birds target the closely coupled priorities of economy and leg safety as the direct imperatives of control, with adequate stability achieved through appropriately tuned intrinsic dynamics

Running into a Trap: Numerical Design of Task-Optimal Preflex Behaviors for Delayed Disturbance Responses

Abstract: 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