Deep Bayesian Nonparametric Learning of Rules and Plans from Demonstrations with a Learned Automaton Prior
<jats:p>We introduce a method to learn imitative policies from expert demonstrations that are interpretable and manipulable. We achieve interpretability by modeling the interactions between high-level actions as an automaton with connections to formal logic. We achieve manipulability by integr...
Main Authors: | Araki, Brandon, Vodrahalli, Kiran, Leech, Thomas, Vasile, Cristian-Ioan, Donahue, Mark, Rus, Daniela |
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Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | English |
Published: |
Association for the Advancement of Artificial Intelligence (AAAI)
2021
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Online Access: | https://hdl.handle.net/1721.1/135230 |
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