Augmenting Policy Learning with Routines Discovered from a Single Demonstration

Augmenting Policy Learning with Routines Discovered from a Single Demonstration

<jats:p>Humans can abstract prior knowledge from very little data and use it to boost skill learning. In this paper, we propose routine-augmented policy learning (RAPL), which discovers routines composed of primitive actions from a single demonstration and uses discovered routines to augment p...

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Bibliographic Details
Main Authors: Zhao, Zelin, Gan, Chuang, Wu, Jiajun, Guo, Xiaoxiao, Tenenbaum, Joshua B
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Format: Article
Language:English
Published: Association for the Advancement of Artificial Intelligence (AAAI) 2023
Online Access:https://hdl.handle.net/1721.1/150390

Internet

https://hdl.handle.net/1721.1/150390

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