Do what i want, not what i did: Imitation of skills by planning sequences of actions
Chris Paxton, Felix Jonathan, Marin Kobilarov, Gregory D. Hager
Abstract
We propose a learning-from-demonstration approach for grounding actions from expert data and an algorithm for using these actions to perform a task in new environments. Our approach is based on an application of sampling-based motion planning to search through the tree of discrete, high-level actions constructed from a symbolic representation of a task. Recursive sampling-based planning is used to explore the space of possible continuous-space instantiations of these actions. We demonstrate the utility of our approach with a magnetic structure assembly task, showing that the robot can intelligently select a sequence of actions in different parts of the workspace and in the presence of obstacles. This approach can better adapt to new environments by selecting the correct high-level actions for the particular environment while taking human preferences into account.
BibTeX
@inproceedings{iros2016_dowhatiwantnotwh,
title = {Do what i want, not what i did: Imitation of skills by planning sequences of actions},
author = {Chris Paxton and Felix Jonathan and Marin Kobilarov and Gregory D. Hager},
booktitle = {IROS 2016},
year = {2016}
}