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Differentiable simulation is a promising toolkit for fast gradient-based policy optimization and system identification.
Completely derandomized self-adaptation in evolution strategies
N. Hansen and A. Ostermeier · 2001
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Learning to slide unknown objects with differentiable physics simulations
C. Song and A. Boularias · 2005
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The cma evolution strategy: a comparing review
N. Hansen · 2006
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Gaussian processes for machine learning , volume 2
C. K. Williams and C. E. Rasmussen · 2006
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Combining evolution strategy and gradient descent method for discriminative learning of bayesian classifiers
X. Chen, X. Liu, and Y. Jia · 2009
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Comparing results of 31 algorithms from the black-box optimization benchmarking bbob-2009
N. Hansen, A. Auger, R. Ros, S. Finck, and P. Pošík · 2010
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Taking the Human Out of the Loop: A Review of Bayesian Optimization
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas · 2016
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End-to-end differentiable physics for learning and control
F. de Avila Belbute-Peres, K. Smith, K. Allen, J. Tenenbaum, and J. Z. Kolter · 2018
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A moving least squares material point method with displacement discontinuity and two-way rigid body coupling
Y. Hu, Y. Fang, Z. Ge, Z. Qu, Y. Zhu, A. Pradhana, and C. Jiang · 2018
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GPyTorch: Blackbox Matrix-matrix Gaussian Process Inference with GPU Acceleration
J. Gardner, G. Pleiss, K. Q. Weinberger, D. Bindel, and A. G. Wilson · 2018
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A differentiable physics engine for deep learning in robotics
J. Degrave, M. Hermans, J. Dambre, et al · 2019
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Chainqueen: A real-time differentiable physical simulator for soft robotics
Y. Hu, J. Liu, A. Spielberg, J. B. Tenenbaum, W. T. Freeman, J. Wu, D. Rus, and W. Matusik · 2019
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Differentiable cloth simulation for inverse problems
J. Liang, M. Lin, and V. Koltun · 2019
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Differentiable fluid simulations for deep learning
N. Thuerey · 2019
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Add: Analytically differentiable dynamics for multi-body systems with frictional contact
M. Geilinger, D. Hahn, J. Zehnder, M. Bächer, B. Thomaszewski, and S. Coros · 2020
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Scalable differentiable physics for learning and control
Y.-L. Qiao, J. Liang, V. Koltun, and M. Lin · 2020
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Difftaichi: Differentiable programming for physical simulation
Y. Hu, L. Anderson, T.-M. Li, Q. Sun, N. Carr, J. Ragan-Kelley, and F. Durand · 2020
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Differentiable molecular simulations for control and learning
W. Wang, S. Axelrod, and R. Gómez-Bombarelli · 2020
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Pods: Policy optimization via differentiable simulation
M. A. Z. Mora, M. P. Peychev, S. Ha, M. Vechev, and S. Coros · 2021
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Fast and feature-complete differentiable physics engine for articulated rigid bodies with contact constraints
K. Werling, D. Omens, J. Lee, I. Exarchos, and C. K. Liu · 2021
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Brax - a differentiable physics engine for large scale rigid body simulation
C. D. Freeman, E. Frey, A. Raichuk, S. Girgin, I. Mordatch, and O. Bachem · 2021
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DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting
E. Heiden, M. Macklin, Y. S. Narang, D. Fox, A. Garg, and F. Ramos · 2021
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Diffaqua: A differentiable computational design pipeline for soft underwater swimmers with shape interpolation
P. Ma, T. Du, J. Z. Zhang, K. Wu, A. Spielberg, R. K. Katzschmann, and W. Matusik · 2021
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Jax m.d. a framework for differentiable physics
S. S. Schoenholz and E. D. Cubuk · 2020
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BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
M. Balandat, B. Karrer, D. R. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy · 2020
Cited alongside, same era.
gradsim: Differentiable simulation for system identification and visuomotor control
K. M. Jatavallabhula, M. Macklin, F. Golemo, V. Voleti, L. Petrini, M. Weiss, B. Considine, J. Parent-Levesque, K. Xie, K. Erleben, L. Paull, F. Shkurti, D. Nowrouzezahrail, and S. Fidler · 2021
Cited alongside, same era.
PlasticineLab: A soft-body manipulation benchmark with differentiable physics
Z. Huang, Y. Hu, T. Du, S. Zhou, H. Su, J. B. Tenenbaum, and C. Gan · 2021
Cited alongside, same era.
Efficient differentiable simulation of articulated bodies
Y.-L. Qiao, J. Liang, V. Koltun, and M. C. Lin · 2021
Cited alongside, same era.
Differentiable physics models for real-world offline model-based reinforcement learning
M. Lutter, J. Silberbauer, J. Watson, and J. Peters · 2021
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(global) optimization: Historical notes and recent developments
M. Locatelli and F. Schoen · 2021
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X. Lin, Z. Huang, Y. Li, J. B. Tenenbaum, D. Held, and C. Gan · 2022
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P. Sundaresan, R. Antonova, and J. Bohg · 2022
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P. Ma, T. Du, J. B. Tenenbaum, W. Matusik, and C. Gan · 2022
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Do differentiable simulators give better policy gradients?
H. J. T. Suh, M. Simchowitz, K. Zhang, and R. Tedrake · 2022
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Dojo: A differentiable simulator for robotics
A. H. Taylor, S. Le Cleac’h, Z. Kolter, M. Schwager, and Z. Manchester · 2022
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Warp: A high-performance python framework for gpu simulation and graphics
M. Macklin · 2022
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