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...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2021
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| Online Access: | https://hdl.handle.net/1721.1/137619 |