Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing
© 2018 IEEE. An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control. In this paper, we build such a simulator for two scenarios, planar pushing and ball bouncing, by augmenting an analytical rigid-bod...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE,
2021-11-08T16:58:37Z.
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| Subjects: | |
| Online Access: | Get fulltext |