Learning of Causal Observable Functions for Koopman-DFL Lifting
Linearization of Nonlinear Controlled Systems and Its Application to
Excavation Automation

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Learning of Causal Observable Functions for Koopman-DFL Lifting Linearization of Nonlinear Controlled Systems and Its Application to Excavation Automation

IEEE Robotics and Automation Letters (RA-L), 2021

5 April 2021

Nicholas Stearns

S. M. I. H. Harry Asada

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Papers citing "Learning of Causal Observable Functions for Koopman-DFL Lifting Linearization of Nonlinear Controlled Systems and Its Application to Excavation Automation"

5 / 5 papers shown

Title
KoopMotion: Learning Almost Divergence Free Koopman Flow Fields for Motion Planning Alice K. Li T. Silva Victoria Edwards Vijay Kumar M. Hsieh 48 0 0 11 Sep 2025
Koopman Operators in Robot Learning Lu Shi Masih Haseli Giorgos Mamakoukas Daniel Bruder Ian Abraham Todd Murphey Jorge Cortes Konstantinos Karydis AI4CE 288 17 0 08 Aug 2024
On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation SkillsConference on Robot Learning (CoRL), 2023 Yunhai Han Mandy Xie Ye Zhao Harish Ravichandar 172 24 0 23 Mar 2023
Data-Driven Encoding: A New Numerical Method for Computation of the Koopman OperatorIEEE Robotics and Automation Letters (RA-L), 2023 Jerry Ng H. Asada 113 6 0 16 Jan 2023
Global, Unified Representation of Heterogenous Robot Dynamics Using Composition Operators: A Koopman Direct Encoding MethodIEEE/ASME transactions on mechatronics (TAM), 2022 Harry Asada 129 17 0 30 Jul 2022

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