Quadruped animals seamlessly transition between gaits as they change locomotion speeds. While the most widely accepted explanation for gait transitions is energy efficiency, there is no clear consensus on the determining factor, nor on the potential effects from terrain properties. In this article, we propose that viability, i.e. the avoidance of falls, represents an important criterion for gait transitions. We investigate the emergence of gait transitions through the interaction between supraspinal drive (brain), the central pattern generator in the spinal cord, the body, and exteroceptive sensing by leveraging deep reinforcement learning and robotics tools. Consistent with quadruped animal data, we show that the walk-trot gait transition for quadruped robots on flat terrain improves both viability and energy efficiency. Furthermore, we investigate the effects of discrete terrain (i.e. crossing successive gaps) on imposing gait transitions, and find the emergence of trot-pronk transitions to avoid non-viable states. Compared with other potential criteria such as peak forces and energy efficiency, viability is the only improved factor after gait transitions on both flat and discrete gap terrains, suggesting that viability could be a primary and universal objective of gait transitions, while other criteria are secondary objectives and/or a consequence of viability. Moreover, we deploy our learned controller in sim-to-real hardware experiments and demonstrate state-of-the-art quadruped agility in challenging scenarios, where the Unitree A1 quadruped autonomously transitions gaits between trot and pronk to cross consecutive gaps of up to 30 cm (83.3 % of the body-length) at over 1.3 m/s.

翻译：四足动物在改变运动速度时能无缝转换步态。尽管对步态转换最广为接受的解释是能量效率，但关于其决定因素以及地形特性的潜在影响尚未形成明确共识。本文提出生存性（即避免跌倒）是步态转换的重要标准。通过利用深度强化学习和机器人技术，我们研究脑源性驱动（大脑）、脊髓中枢模式发生器、本体与外感受传感之间的相互作用如何催生步态转换。与四足动物数据一致，我们证明平坦地形中四足机器人的行走-小跑步态转换既提高了生存性也优化了能量效率。此外，我们探究离散地形（即连续跨越间隙）对强制步态转换的影响，并发现为避免不可生存状态会出现小跑-腾跃步态转换。与峰值力、能量效率等其他潜在标准相比，生存性是在平坦和离散间隙地形中步态转换后唯一显著提升的指标，这表明生存性可能是步态转换的首要通用目标，而其他标准属于次要目标或生存性的衍生结果。进一步地，我们在仿真到现实的硬件实验中部署所学控制器，在挑战性场景中展现出先进的四足敏捷性：Unitree A1四足机器人以超过1.3米/秒的速度自主完成小跑与腾跃间的步态转换，连续跨越长达30厘米（占体长83.3%）的间隙。