Generalizing Over Uncertain Dynamics for Online Trajectory Generation

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 observati...

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Bibliographic Details
Main Authors: Kim, Beomjoon (Author), Kim, Albert (Author), Dai, Hongkai (Author), Kaelbling, Leslie (Author), Lozano-Perez, Tomas (Author)
Format: Article
Language:English
Published: Springer Nature, 2021-11-05T20:35:48Z.
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