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- W3089596396 abstract "This paper presents a novel episodic method to learn a robot's nonlinear dynamics model and an increasingly optimal control sequence for a set of tasks. The method is based on the Koopman operator approach to nonlinear dynamical systems analysis, which models the flow of observables in a function space, rather than a flow in a state space. Practically, this method estimates a nonlinear diffeomorphism that lifts the dynamics to a higher dimensional space where they are linear. Efficient Model Predictive Control methods can then be applied to the lifted model. This approach allows for real time implementation in on-board hardware, with rigorous incorporation of both input and state constraints during learning. We demonstrate the method in a real-time implementation of fast multirotor landing, where the nonlinear ground effect is learned and used to improve landing speed and quality." @default.
- W3089596396 created "2020-10-08" @default.
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- W3089596396 date "2020-05-01" @default.
- W3089596396 modified "2023-09-23" @default.
- W3089596396 title "Episodic Koopman Learning of Nonlinear Robot Dynamics with Application to Fast Multirotor Landing" @default.
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- W3089596396 doi "https://doi.org/10.1109/icra40945.2020.9197510" @default.
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