Imitation learning (IL) enables robots to acquire human-like motion skills from demonstrations, but it still requires extensive high-quality data and retraining to handle complex or long-horizon tasks. To improve data efficiency and adaptability, this study proposes a hierarchical IL framework that integrates motion primitives with proportion-based motion synthesis. The proposed method employs a two-layer architecture, where the upper layer performs long-term planning, while a set of lower-layer models learn individual motion primitives, which are combined according to specific proportions. Three model variants are introduced to explore different trade-offs between learning flexibility, computational cost, and adaptability: a learning-based proportion model, a sampling-based proportion model, and a playback-based proportion model, which differ in how the proportions are determined and whether the upper layer is trainable. Through real-robot pick-and-place experiments, the proposed models successfully generated complex motions not included in the primitive set. The sampling-based and playback-based proportion models achieved more stable and adaptable motion generation than the standard hierarchical model, demonstrating the effectiveness of proportion-based motion integration for practical robot learning.

翻译：模仿学习使机器人能够从演示中习得类人运动技能，但仍需要大量高质量数据和重新训练以处理复杂或长时程任务。为提高数据效率和适应性，本研究提出一种分层模仿学习框架，将运动基元与基于比例的运动合成相结合。该方法采用双层架构：上层执行长期规划，而一组下层模型学习独立的运动基元，这些基元根据特定比例进行组合。本文引入了三种模型变体以探索学习灵活性、计算成本与适应性之间的不同权衡：基于学习的比例模型、基于采样的比例模型以及基于回放的比例模型，它们在比例确定方式及上层是否可训练方面存在差异。通过真实机器人抓放实验，所提模型成功生成了未包含在基元集中的复杂运动。基于采样和基于回放的比例模型相比标准分层模型实现了更稳定、适应性更强的运动生成，验证了基于比例的运动集成在实际机器人学习中的有效性。