Data scarcity remains a fundamental challenge in robot learning. While human demonstrations benefit from abundant motion capture data and vast internet resources, robotic manipulation suffers from limited training examples. To bridge this gap between human and robot manipulation capabilities, we propose UniPrototype, a novel framework that enables effective knowledge transfer from human to robot domains via shared motion primitives. ur approach makes three key contributions: (1) We introduce a compositional prototype discovery mechanism with soft assignments, enabling multiple primitives to co-activate and thus capture blended and hierarchical skills; (2) We propose an adaptive prototype selection strategy that automatically adjusts the number of prototypes to match task complexity, ensuring scalable and efficient representation; (3) We demonstrate the effectiveness of our method through extensive experiments in both simulation environments and real-world robotic systems. Our results show that UniPrototype successfully transfers human manipulation knowledge to robots, significantly improving learning efficiency and task performance compared to existing approaches.The code and dataset will be released upon acceptance at an anonymous repository.

翻译：数据稀缺性仍然是机器人学习领域的根本性挑战。尽管人类示范得益于丰富的运动捕捉数据和海量的互联网资源，但机器人操作却受限于训练样本的不足。为弥合人类与机器人操作能力之间的差距，本文提出UniPrototype——一种通过共享运动基元实现人机领域间有效知识迁移的创新框架。本方法包含三项核心贡献：（1）提出具有软分配机制的组合式原型发现方法，允许多个基元协同激活，从而捕捉混合型与层次化技能；（2）设计自适应原型选择策略，可依据任务复杂度自动调整原型数量，确保表征的可扩展性与高效性；（3）通过在仿真环境与真实机器人系统中的大量实验验证了方法的有效性。实验结果表明，相较于现有方法，UniPrototype成功将人类操作知识迁移至机器人领域，显著提升了学习效率与任务性能。代码与数据集将在论文录用后发布于匿名存储库。