@inproceedings{62ebd58341094a82b76d513d3ba25f1f,
title = "Imitation learning as cause-effect reasoning",
abstract = "We propose a framework for general-purpose imitation learning centered on cause-effect reasoning. Our approach infers a hierarchical representation of a demonstrator{\textquoteright}s intentions, which can explain why they acted as they did. This enables rapid generalization of the observed actions to new situations. We employ a novel causal inference algorithm with formal guarantees and connections to automated planning. Our approach is implemented and validated empirically using a physical robot, which successfully generalizes skills involving bimanual manipulation of composite objects in 3D. These results suggest that cause-effect reasoning is an effective unifying principle for cognitive-level imitation learning.",
keywords = "Artificial general intelligence, Cause effect reasoning, Cognitive robotics, Imitation learning, Parsimonious covering theory",
author = "Garrett Katz and Huang, {Di Wei} and Rodolphe Gentili and James Reggia",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 9th International Conference on Artificial General Intelligence, AGI 2016 ; Conference date: 16-07-2016 Through 19-07-2016",
year = "2016",
doi = "10.1007/978-3-319-41649-6_7",
language = "English (US)",
isbn = "9783319416489",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "64--73",
editor = "Bas Steunebrink and Pei Wang and Ben Goertzel",
booktitle = "Artificial General Intelligence - 9th International Conference, AGI 2016, Proceedings",
}