Motion trajectories offer reliable references for physics-based motion learning but suffer from sparsity, particularly in regions that lack sufficient data coverage. To address this challenge, we introduce a self-supervised, structured representation and generation method that extracts spatial-temporal relationships in periodic or quasi-periodic motions. The motion dynamics in a continuously parameterized latent space enable our method to enhance the interpolation and generalization capabilities of motion learning algorithms. The motion learning controller, informed by the motion parameterization, operates online tracking of a wide range of motions, including targets unseen during training. With a fallback mechanism, the controller dynamically adapts its tracking strategy and automatically resorts to safe action execution when a potentially risky target is proposed. By leveraging the identified spatial-temporal structure, our work opens new possibilities for future advancements in general motion representation and learning algorithms.

翻译：运动轨迹为基于物理学的运动学习提供了可靠参考，但在数据覆盖不足的区域存在稀疏性问题。针对这一挑战，我们提出了一种自监督的结构化表示与生成方法，用于提取周期或准周期运动中的时空关系。连续参数化潜空间中的运动动力学使我们的方法能够增强运动学习算法的插值与泛化能力。基于运动参数化的运动学习控制器能够在线跟踪包括训练中未见目标在内的广泛运动范围。通过引入回退机制，该控制器可动态调整其跟踪策略，并在检测到潜在危险目标时自动切换至安全动作执行。通过利用识别出的时空结构，我们的工作为通用运动表示与学习算法的未来发展开辟了新可能性。