Deep reinforcement learning (DRL) has revolutionised quadruped robot locomotion, but existing control frameworks struggle to generalise beyond their training-induced observational scope, resulting in limited adaptability. In contrast, animals achieve exceptional adaptability through gait transition strategies, diverse gait utilisation, and seamless adjustment to immediate environmental demands. Inspired by these capabilities, we present a novel DRL framework that incorporates key attributes of animal locomotion: gait transition strategies, pseudo gait procedural memory, and adaptive motion adjustments. This approach enables our framework to achieve unparalleled adaptability, demonstrated through blind zero-shot deployment on complex terrains and recovery from critically unstable states. Our findings offer valuable insights into the biomechanics of animal locomotion, paving the way for robust, adaptable robotic systems.

翻译：深度强化学习（DRL）已彻底改变了四足机器人运动控制，但现有框架难以泛化至其训练诱导的观测范围之外，导致适应性受限。相比之下，动物通过步态转换策略、多样化步态运用以及对即时环境需求的无缝调整，实现了卓越的适应性。受此启发，我们提出一种新型DRL框架，该框架融合了动物运动的关键特性：步态转换策略、伪步态程序性记忆及自适应运动调节。该方法使我们的框架实现了无与伦比的适应性，通过在复杂地形上的盲零样本部署以及从临界不稳定状态中恢复的能力得到验证。我们的研究为动物运动生物力学提供了重要见解，为构建稳健、自适应的机器人系统开辟了新途径。