用统一原型让机器人从人类动作中学技能，提升学习效率。
UniPrototype: Humn-Robot Skill Learning with Uniform Prototypes
- 用软分配机制让多个动作原型协同，捕捉复杂技能组合
- 自动调整原型数量匹配任务难易，实现高效可扩展表示
- 在仿真和真实机器人上验证，显著优于现有方法
数据稀缺仍是机器人学习的核心挑战。尽管人类示范可依托丰富的动作捕捉数据和互联网资源，机器人操作却受限于有限的训练样本。为弥合人机操作能力差距，我们提出UniPrototype框架，通过共享运动原型实现人类到机器人的知识迁移。该方法有三项关键贡献：(1) 提出一种组合式原型发现机制与软分配策略，允许多个原型协同激活，从而捕捉混合与分层技能；(2) 设计自适应原型选择策略，自动根据任务复杂度调整原型数量，确保表示的可扩展性与高效性；(3) 在仿真环境与真实机器人系统中开展大量实验，结果表明UniPrototype能有效将人类操作知识迁移到机器人，显著提升学习效率与任务表现。代码与数据集将在论文录用后公开于匿名仓库。
原文摘要 · Abstract (English)
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.
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