ISPIL: Interactive Sub-Goal-Planning Imitation Learning for Long-Horizon Tasks With Diverse Goals

Imitation Learning (IL) is a promising approach for teaching tasks to robots by human demonstrations, although it faces challenges from long-horizon tasks and diverse goals in real-world settings. These issues stem from (i) a distribution mismatch between demonstrations and real-world execution and...

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Bibliografiske detaljer

Principais autores:	Cynthia Ochoa, Hanbit Oh, Yuhwan Kwon, Yukiyasu Domae, Takamitsu Matsubara
Format:	Artigo
Sprog:	Inglês
Udgivet:	IEEE 2024-01-01
Serier:	IEEE Access
Fag:	Interactive imitation learning learning-to-plan hierarchical policy
Online adgang:	https://ieeexplore.ieee.org/document/10811934/
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