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Bridging the Robot Perception Gap with Mid-Level Vision Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Generalizing Over Uncertain Dynamics for Online Trajectory Generation

Authors : Beomjoon Kim, Albert Kim, Hongkai Dai, Leslie Kaelbling, Tomas Lozano-Perez

Published in: Robotics Research

Publisher: Springer International Publishing

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Abstract

We present an algorithm which learns an online trajectory generator that can generalize over varying and uncertain dynamics. When the dynamics is certain, the algorithm generalizes across model parameters. When the dynamics is partially observable, the algorithm generalizes across different observations. To do this, we employ recent advances in supervised imitation learning to learn a trajectory generator from a set of example trajectories computed by a trajectory optimizer. In experiments in two simulated domains, it finds solutions that are nearly as good as, and sometimes better than, those obtained by calling the trajectory optimizer on line. The online execution time is dramatically decreased, and the off-line training time is reasonable.

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previous chapter Deep Feature Learning for Acoustics-Based Terrain Classification
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Footnotes
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Metadata
Title
Generalizing Over Uncertain Dynamics for Online Trajectory Generation
Authors
Beomjoon Kim
Albert Kim
Hongkai Dai
Leslie Kaelbling
Tomas Lozano-Perez
Copyright Year
2018
Book
Robotics Research

Print ISBN: 978-3-319-60915-7

Electronic ISBN: 978-3-319-60916-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-60916-4_3