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RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration.
Brahma
Pavse, Faraz Torabi, Josiah
Hanna, Garrett Warnell, and Peter
Stone.
IEEE Robotics and Automation Letters (RA-L), 5:6262–69, October 2020.
Video
of the experiments; 13-minute video presentation.
[PDF]405.1kB [slides.pptx]115.4MB
Augmenting reinforcement learning with imitation learning is often hailed as a method by which to improve upon learning from scratch. However, most existing methods for integrating these two techniques are subject to several strong assumptions---chief among them that information about demonstrator actions is available. In this paper, we investigate the extent to which this assumption is necessary by introducing and evaluating reinforced inverse dynamics modeling (RIDM), a novel paradigm for combining imitation from observation (IfO) and reinforcement learning with no dependence on demonstrator action information. Moreover, RIDM requires only a single demonstration trajectory and is able to operate directly on raw (unaugmented) state features. We find experimentally that RIDM performs favorably compared to a baseline approach for several tasks in simulation as well as for tasks on a real UR5 robot arm. Experiment videos can be found at https://sites.google.com/view/ridm-reinforced-inverse-dynami.
@article{RAL20-pavse,
author = {Brahma Pavse and Faraz Torabi and Josiah Hanna and Garrett Warnell and Peter Stone},
title = {RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration},
Journal = {{IEEE} Robotics and Automation Letters (RA-L)},
wwwnote={Presented at International Conference on Intelligent Robots and Systems ({IROS})\\
A preliminary version was presented at the <i>Imitation, Intent, and Interaction</i> (I3) Workshop at ICML 2019},
year = {2020},
month = {October},
volume= {5}, issue={4},
pages="6262--69",
issn="2377-3766",
doi="10.1109/LRA.2020.3010750",
wwwnote={<a href="https://sites.google.com/view/ridm-reinforced-inverse-dynami">Video of the experiments</a>; <a href="https://www.youtube.com/watch?v=QDReBWEoDtE">13-minute video presentation</a>.},
abstract = {
Augmenting reinforcement learning with imitation learning is often hailed as a
method by which to improve upon learning from scratch. However, most existing
methods for integrating these two techniques are subject to several strong
assumptions---chief among them that information about demonstrator actions is
available. In this paper, we investigate the extent to which this assumption
is necessary by introducing and evaluating reinforced inverse dynamics
modeling (RIDM), a novel paradigm for combining imitation from observation
(IfO) and reinforcement learning with no dependence on demonstrator action
information. Moreover, RIDM requires only a single demonstration trajectory
and is able to operate directly on raw (unaugmented) state features. We find
experimentally that RIDM performs favorably compared to a baseline approach
for several tasks in simulation as well as for tasks on a real UR5 robot arm.
Experiment videos can be found at
https://sites.google.com/view/ridm-reinforced-inverse-dynami.
},
}
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