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Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as an alternative modeling and learning-based control method across various robotics sub-domains.
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2021
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A. T. Taylor, T. A. Berrueta, and T. D. Murphey, “Active learning in robotics: A review of control principles,” Mechatronics , vol. 77, p. 102576, 2021
2021
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2021
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D. Bruder, X. Fu, R. B. Gillespie, C. D. Remy, and R. Vasudevan, “Koopman-based control of a soft continuum manipulator under variable loading conditions,” IEEE Robotics and Automation Letters (RA-L) , vol. 6, no. 4, pp. 6852–6859, 2021
2021
Cited alongside, same era.
M. Han, J. Euler-Rolle, and R. K. Katzschmann, “Desko: Stability-assured robust control with a deep stochastic Koopman operator,” in International Conference on Learning Representations (ICLR) , 2021
2021
Cited alongside, same era.
L. Shi and K. Karydis, “ACD-EDMD: Analytical construction for dictionaries of lifting functions in Koopman operator-based nonlinear robotic systems,” IEEE Robotics and Automation Letters (RA-L) , vol. 7, no. 2, pp. 906–913, 2021
2021
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C. Folkestad and J. W. Burdick, “Koopman NMPC: Koopman-based learning and nonlinear model predictive control of control-affine systems,” in IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 7350–7356
2021
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J. M. Louw and H. Jordaan, “Data-driven system identification and model predictive control of a multirotor with an unknown suspended payload,” IFAC-PapersOnLine , vol. 54, no. 21, pp. 210–215, 2021
2021
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A. Joglekar, C. Samak, T. Samak, K. C. Kosaraju, J. Smereka, M. Brudnak, D. Gorsich, V. Krovi, and U. Vaidya, “Analytical construction of Koopman EDMD candidate functions for optimal control of ackermann-steered vehicles,” IFAC-PapersOnLine , vol. 56, no. 3, pp. 619–624, 2023
2023
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M. Švec, Š. Ileš, and J. Matuško, “Predictive direct yaw moment control based on the Koopman operator,” IEEE Transactions on Control Systems Technology , vol. 31, no. 6, pp. 2912–2919, 2023
2023
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K. Peng, W. Chen, S. Guan, and Z. Liu, “Hysteresis inversion-free predictive compensation control for soft pneumatic actuators based on a global Koopman modeling strategy,” Physica Scripta , vol. 98, no. 12, p. 125206, 2023
2023
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H. Wang, W. Liang, B. Liang, H. Ren, Z. Du, and Y. Wu, “Robust position control of a continuum manipulator based on selective approach and Koopman operator,” IEEE Transactions on Industrial Electronics , 2023
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2023
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S. S. Narayanan, D. Tellez-Castro, S. Sutavani, and U. Vaidya, “Se (3) Koopman-MPC: Data-driven learning and control of quadrotor uavs,” IFAC-PapersOnLine , vol. 56, no. 3, pp. 607–612, 2023
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J. Jia, W. Zhang, K. Guo, J. Wang, X. Yu, Y. Shi, and L. Guo, “Evolver: Online learning and prediction of disturbances for robot control,” IEEE Transactions on Robotics , vol. 40, pp. 382–402, 2023
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2023
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C. Rodwell, J. Buzhardt, and P. Tallapragada, “A Koopman operator approach for the pitch stabilization of a hydrofoil in an unsteady flow field,” in American Control Conference (ACC) . IEEE, 2023, pp. 1453–1458
2023
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X. Kong, H. Mo, E. Dong, Y. Liu, and D. Sun, “Automatic tracking of surgical instruments with a continuum laparoscope using data-driven control in robotic surgery,” Advanced Intelligent Systems , vol. 5, no. 2, p. 2200188, 2023
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M. Bakhtiaridoust, M. Yadegar, and N. Meskin, “Data-driven fault detection and isolation of nonlinear systems using deep learning for Koopman operator,” ISA transactions , vol. 134, pp. 200–211, 2023
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J. Moyalan, Y. Chen, and U. Vaidya, “Convex approach to data-driven off-road navigation via linear transfer operators,” IEEE Robotics and Automation Letters , vol. 8, no. 6, pp. 3278–3285, 2023
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W. Guo, S. Zhao, H. Cao, B. Yi, and X. Song, “Koopman operator-based driver-vehicle dynamic model for shared control systems,” Applied Mathematical Modelling , vol. 114, pp. 423–446, 2023
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N. Chakraborty, A. Hasan, S. Liu, T. Ji, W. Liang, D. L. McPherson, and K. Driggs-Campbell, “Structural attention-based recurrent variational autoencoder for highway vehicle anomaly detection,” in International Conference on Autonomous Agents and Multiagent Systems , 2023, pp. 1125–1134
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W. Zhan, Z. Miao, Y. Chen, Y. Feng, and Y. Wang, “Koopman operation-based leader-following formation control for nonholonomic mobile robots under denial-of-service attacks,” in IEEE International Conference on Unmanned Systems (ICUS) , 2023, pp. 110–115
2023
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M. Haseli and J. Cortés, “Temporal forward-backward consistency, not residual error, measures the prediction accuracy of extended dynamic mode decomposition,” IEEE Control Systems Letters , vol. 7, pp. 649–654, 2023
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H. N. Esfahani, U. Vaidya, and J. M. Velni, “Performance-oriented data-driven control: Fusing Koopman operator and MPC-based reinforcement learning,” IEEE Control Systems Letters , 2024
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S. Khorshidi, M. Dawood, and M. Bennewitz, “Centroidal state estimation based on the Koopman embedding for dynamic legged locomotion,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2024, pp. 12 832–12 839
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X. Lin, S. Liu, C. Liu, and Y. Wang, “Dynamic modeling of robotic fish considering background flow using Koopman operators,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2024, pp. 11 843–11 848
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S. Pan, E. Kaiser, B. M. de Silva, J. N. Kutz, and S. L. Brunton, “PyKoopman: A python package for data-driven approximation of the Koopman operator,” Journal of Open Source Software , vol. 9, no. 94, p. 5881, 2024
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Z. C. Guo, F. Dümbgen, J. R. Forbes, and T. D. Barfoot, “Data-driven batch localization and slam using Koopman linearization,” IEEE Transactions on Robotics , 2024
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2025
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X. Guan, Y. Wang, X. Kang, W. Yao, J. Zhang, and G. Li, “An online system identification algorithm for spherical robot using the Koopman theory,” IEEE Robotics and Automation Letters , 2025
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L. Han, K. Peng, W. Chen, and Z. Liu, “A data-driven Koopman modeling framework with application to soft robots,” International Journal of Control, Automation and Systems , vol. 23, no. 1, pp. 249–261, 2025
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N. Zhang, Z. Qi, J. Chen, H. Zhang, and H. R. Karimi, “Koopman-based 3-dimensional path following control for robotic flexible needles,” Optimal Control Applications and Methods , vol. 46, no. 2, pp. 459–475, 2025
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