DeepTransition：生存性促使步态转变在学习预判式四足运动技能中涌现
DeepTransition: Viability Leads to the Emergence of Gait Transitions in Learning Anticipatory Quadrupedal Locomotion Skills
AI总结：
该研究提出生存性是四足步态转变的核心判定标准，通过深度强化学习验证了平坦与离散地形下的步态转变规律，并通过仿真到现实迁移在Unitree A1机器人上实现了高敏捷性的自主跨隙步态切换。
摘要：
四足动物在改变运动速度时可在不同步态间无缝切换。尽管步态转变最广为接受的解释是能量效率，但学界对其决定因素以及地形特性的潜在影响尚未达成明确共识。本文提出，生存性（即避免跌倒）是步态转变的一项重要判定标准。我们借助深度强化学习与机器人工具，通过研究脊髓上驱动（大脑）、脊髓中的中枢模式发生器、身体与外感受器感知之间的相互作用，探究步态转变的涌现机制。与四足动物的研究数据一致，我们证明四足机器人在平坦地形上的行走-小跑步态转变可同时提升生存性与能量效率。此外，我们研究了离散地形（即跨越连续间隙）对步态转变的强制作用，发现为避免非生存状态，会涌现出小跑- pronk（弹跳步）转变。与峰值力、能量效率等其他潜在标准相比，在平坦地形和离散间隙地形上，生存性是步态转变后唯一得到提升的指标，这表明生存性可能是步态转变的首要且通用的目标，而其他标准属于次要目标，或是生存性提升带来的结果。此外，我们将训练得到的控制器通过 sim-to-real（仿真到现实）迁移部署到硬件实验中，在挑战性场景中展现了当前最优的四足运动敏捷性：Unitree A1 四足机器人可自主在小跑与弹跳步之间切换步态，以超过1.3 m/s的速度跨越最大达30厘米（占体长的83.3%）的连续间隙。
英文摘要：
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.
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