Salamander-like robots are designed inspired by the skeletal structure of their biological counterparts. However, existing controllers cannot fully exploit these morphological features and largely rely on predefined patterns or joint trajectories, which prevents the generation of diverse and flexible gaits and limits their applicability in real-world scenarios. In this paper, we propose a phase-based learning framework that enables the robot to acquire a diverse repertoire of gaits without using reference motions. Each body part is controlled by a phase variable capable of forward and backward evolution, with a phase coverage reward to promote the exploration of the leg phase space. Additionally, morphological symmetry of the robot is incorporated via data augmentation, improving sample efficiency and enforcing both motion-level and task-level symmetry in learned behaviors. Extensive experiments show that the robot successfully acquires 22 representative gaits exhibiting both dynamic and symmetric movements, demonstrating the effectiveness of the proposed learning framework.

翻译：受生物蝾螈骨骼结构启发，人们设计了蝾螈型机器人。然而，现有控制器无法充分利用这些形态特征，且主要依赖预定义模式或关节轨迹，这阻碍了多样化和灵活步态的生成，并限制了其在现实场景中的应用。本文提出一种基于相位的学习框架，使机器人无需参考运动即可获得多样化的步态技能。每个身体部位由可正向和反向演化的相位变量控制，并设置相位覆盖奖励以促进腿部相位空间的探索。此外，通过数据增强引入机器人形态对称性，提高样本效率，并在习得行为中强制执行运动层级和任务层级的对称性。大量实验表明，机器人成功掌握了22种兼具动态性和对称性的代表性步态，验证了所提学习框架的有效性。