Effective motion representation is crucial for enabling robots to imitate expressive behaviors in real time, yet existing motion controllers often ignore inherent patterns in motion. Previous efforts in representation learning do not attempt to jointly capture structured periodic patterns and irregular variations in human and animal movement. To address this, we present Multi-Domain Motion Embedding (MDME), a motion representation that unifies the embedding of structured and unstructured features using a wavelet-based encoder and a probabilistic embedding in parallel. This produces a rich representation of reference motions from a minimal input set, enabling improved generalization across diverse motion styles and morphologies. We evaluate MDME on retargeting-free real-time motion imitation by conditioning robot control policies on the learned embeddings, demonstrating accurate reproduction of complex trajectories on both humanoid and quadruped platforms. Our comparative studies confirm that MDME outperforms prior approaches in reconstruction fidelity and generalizability to unseen motions. Furthermore, we demonstrate that MDME can reproduce novel motion styles in real-time through zero-shot deployment, eliminating the need for task-specific tuning or online retargeting. These results position MDME as a generalizable and structure-aware foundation for scalable real-time robot imitation.

翻译：有效的运动表示对于使机器人能够实时模仿表达性行为至关重要，然而现有的运动控制器往往忽略了运动中固有的模式。先前在表示学习方面的尝试并未试图联合捕捉人类和动物运动中结构化的周期性模式与不规则变化。为此，我们提出了多域运动嵌入（MDME），这是一种运动表示方法，它通过并行使用基于小波的编码器和概率嵌入，统一了对结构化和非结构化特征的嵌入。该方法能够从最小输入集中生成参考运动的丰富表示，从而提升对不同运动风格和形态的泛化能力。我们通过在机器人控制策略中条件化学习到的嵌入，评估了MDME在无需重定向的实时运动模仿上的表现，证明了其在人形和四足平台上均能准确复现复杂轨迹。我们的比较研究证实，MDME在重建保真度以及对未见运动的泛化能力上优于先前的方法。此外，我们展示了MDME能够通过零样本部署实时复现新颖的运动风格，无需进行任务特定的调优或在线重定向。这些结果使MDME成为可扩展实时机器人模仿的一个具有泛化能力和结构感知能力的基础框架。