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

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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Bibliographic Details
Main Authors: Cynthia Ochoa, Hanbit Oh, Yuhwan Kwon, Yukiyasu Domae, Takamitsu Matsubara
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Interactive imitation learning
learning-to-plan
hierarchical policy
Online Access:https://ieeexplore.ieee.org/document/10811934/
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