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Soft robots hold significant potential in legged locomotion due to their inherent deformability, enabling enhanced adaptability to various environmental conditions and the generation of diverse locomotion gaits. While various soft robots have been proposed for terrestrial locomotion, research on dynamically-stable locomotion, such as trotting, with actuated soft bending limbs remains limited. We introduce a pneumatically-actuated soft quadruped featuring a soft body capable of a variety of dynamically-stable trotting locomotion. We utilize soft limb kinematics and parameterize fundamental limb locomotion to obtain quadrupedal locomotion trajectories for both linear and curvilinear motions. We also employ a physics-enabled dynamic model to optimize and evaluate trotting locomotion trajectories for dynamic stability. We further validate the stable locomotion trajectories through empirical experiments conducted on a soft quadruped prototype. The results demonstrate that the quadruped trots at a peak speed of 1.24 body lengths per second when traversing flat and uneven terrains, including slopes, cluttered areas, and naturalistic irregular surfaces. Furthermore, we compare the energy efficiency between trotting and crawling locomotion. The findings reveal that trotting is significantly more energy-efficient than crawling, with an average energy saving of up to 42%.Note to Practitioners—This paper was motivated by the challenge of achieving dynamically stable and efficient locomotion in soft quadrupeds. Many soft-legged robots are typically designed for statically stable, albeit inefficient and slow, locomotion gaits such as crawling. Our research aims to address this practical challenge of improving mobility in soft-legged robots. We develop a novel soft quadruped with pneumatically-actuated soft limbs that achieves efficient trotting that is 42% more energy-efficient than crawling. This work is particularly relevant for industries requiring adaptable and efficient navigation in environments, such as search and rescue, agricultural monitoring, and exploration. The development and optimization of trotting gaits through a physics-enabled dynamic model for dynamic stability provide a foundational framework for enhancing the adaptability and operational utility of soft robots. While our findings mark a significant step forward, challenges remain in deploying these locomotion strategies on autonomous untethered robots with onboard sensor feedback. Future research will focus on these areas, aiming to improve the practical deployment and robustness of soft robotic locomotive systems. 

Abstract. Global fire-vegetation models are widely used to assessimpacts of environmental change on fire regimes and the carbon cycle and toinfer relationships between climate, land use and fire. However,differences in model structure and parameterizations, in both the vegetationand fire components of these models, could influence overall modelperformance, and to date there has been limited evaluation of how welldifferent models represent various aspects of fire regimes. The Fire ModelIntercomparison Project (FireMIP) is coordinating the evaluation ofstate-of-the-art global fire models, in order to improve projections of firecharacteristics and fire impacts on ecosystems and human societies in thecontext of global environmental change. Here we perform a systematicevaluation of historical simulations made by nine FireMIP models to quantifytheir ability to reproduce a range of fire and vegetation benchmarks. TheFireMIP models simulate a wide range in global annual total burnt area(39–536 Mha) and global annual fire carbon emission (0.91–4.75 Pg C yr−1) for modern conditions (2002–2012), but most of the range in burntarea is within observational uncertainty (345–468 Mha). Benchmarking scoresindicate that seven out of nine FireMIP models are able to represent thespatial pattern in burnt area. The models also reproduce the seasonality inburnt area reasonably well but struggle to simulate fire season length andare largely unable to represent interannual variations in burnt area.However, models that represent cropland fires see improved simulation offire seasonality in the Northern Hemisphere. The three FireMIP models whichexplicitly simulate individual fires are able to reproduce the spatialpattern in number of fires, but fire sizes are too small in key regions, andthis results in an underestimation of burnt area. The correct representationof spatial and seasonal patterns in vegetation appears to correlate with abetter representation of burnt area. The two older fire models included inthe FireMIP ensemble (LPJ–GUESS–GlobFIRM, MC2) clearly perform less wellglobally than other models, but it is difficult to distinguish between theremaining ensemble members; some of these models are better at representingcertain aspects of the fire regime; none clearly outperforms all othermodels across the full range of variables assessed.