English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper presents a novel episodic method to learn a robot's nonlineardynamics model and an increasingly optimal control sequence for a set of tasks.The method is based on the {\em Koopman operator} approach to nonlineardynamical systems analysis, which models the flow of {\em observables} in afunction space, rather than a flow in a state space. Practically, this methodestimates a nonlinear diffeomorphism that lifts the dynamics to a higherdimensional space where they are linear. Efficient Model Predictive Controlmethods can then be applied to the lifted model. This approach allows for realtime implementation in on-board hardware, with rigorous incorporation of bothinput and state constraints during learning. We demonstrate the method in areal-time implementation of fast multirotor landing, where the nonlinear groundeffect is learned and used to improve landing speed and quality.

Episodic Koopman Learning of Nonlinear Robot Dynamics with Application to Fast Multirotor Landing