UniPrototype: Humn-Robot Skill Learning with Uniform Prototypes
This addresses the challenge of limited training data for robotic manipulation, enabling more efficient skill transfer from humans to robots, though it appears incremental in its approach.
The paper tackles the problem of data scarcity in robot learning by proposing UniPrototype, a framework for transferring human manipulation knowledge to robots via shared motion primitives, resulting in significant improvements in learning efficiency and task performance compared to existing methods.
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.