Deep Bayesian Nonparametric Learning of Rules and Plans from Demonstrations with a Learned Automaton Prior
Name
aaai20.pdf
Description
Accepted version
Size
1.49 MB
Format
Adobe PDF
Checksum (MD5)
ec9946cc072eab6b5607ba4d3980f157
Author(s)
Araki, Brandon
Vodrahalli, Kiran
Leech, Thomas
Vasile, Cristian-Ioan
Donahue, Mark
Rus, Daniela
Date Issued
2020
Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Version
Author's final manuscript
Abstract
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 integrating this automaton into planning, so that changes to the automaton have predictable effects on the learned behavior. These qualities allow a human user to first understand what the model has learned, and then either correct the learned behavior or zero-shot generalize to new, similar tasks. We build upon previous work by no longer requiring additional supervised information which is hard to collect in practice. We achieve this by using a deep Bayesian nonparametric hierarchical model. We test our model on several domains and also show results for a real-world implementation on a mobile robotic arm platform.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Lincoln Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1609/AAAI.V34I06.6559
