Seamless multi-skill learning: learning and transitioning non-similar skills in quadruped robots with limited data

In multi-skill imitation learning for robots, expert datasets with complete motion features are crucial for enabling robots to learn and transition between different skills. However, such datasets are often difficult to obtain. As an alternative, datasets constructed using only joint positions are m...

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Bibliografske podrobnosti

Principais autores:	Jiaxin Tu, Peng Zhai, Yueqi Zhang, Xiaoyi Wei, Zhiyan Dong, Lihua Zhang
Format:	Artigo
Jezik:	Inglês
Izdano:	Frontiers Media S.A. 2025-04-01
Serija:	Frontiers in Robotics and AI
Teme:	multi-skill learning imitation learning adaptive command sampling self-trajectory augmentation quadrupedal robots
Online dostop:	https://www.frontiersin.org/articles/10.3389/frobt.2025.1542692/full
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