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

Mitigating memory effects during undulatory locomotion on hysteretic materials dataset

Tracked snake data CoccipitalisMidlineCoords are .mat files with the 2D planar coordinates of the midline of Chionactis occipitalis snakes moving on the surface of 300 micron glass beads. CoccipitalisLiftingMidlineCoords are .mat files with the 3D coordinates of the midline of Chionactis occipitalis snakes moving on the surface of 300 micron glass beads.Undulatory swimming in flowing media like water is well studied, but little is known about locomotion in environments that are permanently deformed by body-substrate interactions like snakes in sand, eels in mud, and nematode worms in rotting fruit. We study the desert-specialist snake Chionactis occipitalis traversing granular matter and find body inertia is negligible despite rapid transit. New surface resistive force theory (RFT) calculation reveals this snake's waveform minimizes material memory effects and optimizes speed given anatomical limitations (power). RFT explains the morphology and waveform dependent performance of a diversity of non-sand-specialists, but over-predicts the capability of snakes with high slip. Robophysical experiments recapitulate aspects of these failure-prone snakes, elucidating how reencountering previously remodeled material hinders performance. This study reveals how memory effects stymied the locomotion of snakes in our previous study [Marvi et al, Science, 2014] and suggests the existence of a predictive model for history-dependent locomotion.This work was funded by NSF PoLS PHY-1205878, PHY-1150760, and CMMI-1361778. ARO W911NF-11-1-0514, U.S. DoD, NDSEG 32 CFR 168a (P.E.S.), and the Simons Southeast Center for Mathematics and Biolog