Learning stabilizable nonlinear dynamics with contraction-based regularization
© The Author(s) 2020. We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics. The key contribution is a control-theoretic regularizer for dynamics fitting rooted in the notion of stabilizability, a constraint which guarantees the e...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publications
2022
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| Online Access: | https://hdl.handle.net/1721.1/139675 |