让蝾螈机器人自主学会22种灵活步态，无需预设动作
Phase-Based Multi-Gait Learning for a Salamander-Like Robot
- 用相位变量控制身体各部分，支持正反演化以探索步态空间
- 在无参考运动情况下学习出22种具动态对称性的步态
- 通过形态对称数据增强提升样本效率，适合仿生机器人研究
蝾螈类机器人设计受其生物原型骨骼结构启发。然而，现有控制器无法充分挖掘这些形态特征，主要依赖预设模式或关节轨迹，导致生成的步态种类有限且缺乏灵活性，限制了其在真实场景中的应用。本文提出一种基于相位的学习框架，使机器人可在无参考运动的情况下自主习得多样步态。每个身体部件由可正向与反向演化的相位变量控制，并引入相位覆盖奖励以促进腿部相位空间的探索。同时，通过数据增强融入机器人形态对称性，提升样本效率，并在运动和任务层面强制对称性。大量实验表明，该机器人成功习得22种具有动态与对称特性的代表性步态，验证了所提框架的有效性。
原文摘要 · Abstract (English)
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.
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