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PyPose is an open-source library for robot learning.
A. Wächter and L. T. Biegler, “On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming,”
2006
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D. Simon,
2006
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J. A. E. Andersson, J. Gillis, G. Horn, J. B. Rawlings, and M. Diehl, “CasADi – A software framework for nonlinear optimization and optimal control,”
2018
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A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” in
2019
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J. Pohlodek, B. Morabito, C. Schlauch, P. Zometa, and R. Findeisen, “Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via hilo-mpc,” 2022
2022
Cited alongside, same era.
2023
Cited alongside, same era.
T. Fu, S. Su, and C. Wang, “iSLAM: Imperative SLAM,”
2023
Cited alongside, same era.
A. Pandey, D. Huang, Y. Yu, and J. Geng, “Learning koopman operators with control using bi-level optimization,” in
2023
Closest in time.
F. Yang, C. Wang, C. Cadena, and M. Hutter, “iplanner: Imperative path planning,” in
2023
Closest in time.
A. Tuor, J. Drgona, J. Koch, M. Shapiro, D. Vrabie, and S. Briney, “NeuroMANCER: Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations,” 2023
2023
Closest in time.
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