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：摘要：四足动物在改变运动速度时能无缝地转换步态。尽管能量效率是步态转变最广为接受的解释，但其决定性因素以及地形特性可能带来的影响尚无明确共识。本文提出，生存性（即避免摔倒）是步态转变的重要准则。通过利用深度强化学习和机器人技术，我们研究了大脑（脑上驱动）、脊髓中央模式发生器、身体以及外部感知之间的交互作用如何促使步态转变的涌现。与四足动物数据一致，我们发现四足机器人在平地上进行走-小跑步态转变既能提升生存性也能提高能量效率。此外，我们研究了离散地形（即连续跨越间隙）对强制步态转变的影响，并观察到小跑-奔跑步态转变以规避不可生存状态。与峰值力、能量效率等其他潜在准则相比，在平地和离散间隙地形上，步态转变后唯一显著改善的指标是生存性，这表明生存性可能是步态转变的首要且普适目标，而其他指标则为次要目标或生存性带来的结果。最后，我们将所学控制器部署到仿真到真实硬件的实验中，在挑战性场景下展示了当前最优的四足敏捷性：Unitree A1四足机器人以超过1.3米/秒的速度自主在步态间转换（小跑与奔跑），连续跨越最大达30厘米（占体长83.3%）的间隙。