Fetching the paper…
Reading the bibliography…
Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation parameters that best fit the observed sensor readings.
1905
Earlier work this paper cites.
J. Baumgarte, “Stabilization of constraints and integrals of motion in dynamical systems,” Computer methods in applied mechanics and engineering , vol. 1, no. 1, pp. 1–16, 1972
1972
Earlier work this paper cites.
K. H. Hunt and F. R. E. Crossley, “Coefficient of restitution interpreted as damping in vibroimpact,” 1975
1975
Earlier work this paper cites.
A. W. Moore, “Fast, robust adaptive control by learning only forward models,” in Proceedings of the 4th International Conference on Neural Information Processing Systems , ser. NIPS’91. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1991, p. 571–578
1991
Earlier work this paper cites.
S. Schaal and C. G. Atkeson, “Robot juggling: implementation of memory-based learning,” IEEE Control Systems Magazine , vol. 14, no. 1, pp. 57–71, 1994
1994
Earlier work this paper cites.
D. Stewart and J. C. Trinkle, “An implicit time-stepping scheme for rigid body dynamics with coulomb friction,” in Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065) , vol. 1. IEEE, 2000, pp. 162–169
2000
Earlier work this paper cites.
R. Featherstone, Rigid Body Dynamics Algorithms . Berlin, Heidelberg: Springer-Verlag, 2007
2007
Earlier work this paper cites.
G. Guennebaud, B. Jacob, et al. , “Eigen v3,” http://eigen.tuxfamily.org, 2010
2010
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2012, pp. 5026–5033
2012
Earlier work this paper cites.
N. Simon, J. Friedman, T. Hastie, and R. Tibshirani, “A sparse-group lasso,” Journal of computational and graphical statistics , vol. 22, no. 2, pp. 231–245, 2013
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
P. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, et al. , “Interaction networks for learning about objects, relations and physics,” in Advances in Neural Information Processing Systems , 2016, pp. 4502–4510
2016
Earlier work this paper cites.
K.-T. Yu, M. Bauza, N. Fazeli, and A. Rodriguez, “More than a million ways to be pushed. a high-fidelity experimental dataset of planar pushing,” in 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2016, pp. 30–37
2016
Earlier work this paper cites.
D.-A. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and accurate deep network learning by exponential linear units (elus),” 2016
2016
Earlier work this paper cites.
M. Giftthaler, M. Neunert, M. Stäuble, M. Frigerio, C. Semini, and J. Buchli, “Automatic differentiation of rigid body dynamics for optimal control and estimation,” Advanced Robotics , vol. 31, no. 22, pp. 1225–1237, 2017
2017
Earlier work this paper cites.
J. R. Leal and B. Bell, “joaoleal/CppADCodeGen: CppAD 2017,” July 2017. [Online]. Available: https://doi.org/10.5281/zenodo.836832
2017
Earlier work this paper cites.
J. Lee, M. X. Grey, S. Ha, T. Kunz, S. Jain, Y. Ye, S. S. Srinivasa, M. Stilman, and C. K. Liu, “Dart: Dynamic animation and robotics toolkit,” Journal of Open Source Software , vol. 3, no. 22, p. 500, 2018. [Online]. Available: https://doi.org/10.21105/joss.00500
2018
Earlier work this paper cites.
A. Ajay, J. Wu, N. Fazeli, M. Bauza, L. P. Kaelbling, J. B. Tenenbaum, and A. Rodriguez, “Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018
2018
Earlier work this paper cites.
F. Golemo, A. A. Taiga, A. Courville, and P.-Y. Oudeyer, “Sim-to-real transfer with neural-augmented robot simulation,” in Proceedings of The 2nd Conference on Robot Learning , ser. Proceedings of Machine Learning Research, A. Billard, A. Dragan, J. Peters, and J. Morimoto, Eds., vol. 87. PMLR, 29–31 Oct 2018, pp. 817–828. [Online]. Available: http://proceedings.mlr.press/v87/golemo18a.html
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Carpentier and N. Mansard, “Analytical derivatives of rigid body dynamics algorithms,” in Robotics: Science and Systems , 2018
2018
Earlier work this paper cites.
F. de Avila Belbute-Peres, K. Smith, K. Allen, J. Tenenbaum, and J. Z. Kolter, “End-to-end differentiable physics for learning and control,” in Advances in Neural Information Processing Systems 31 , S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds. Curran Associates, Inc., 2018, pp. 7178–7189. [Online]. Available: http://papers.nips.cc/paper/7948-end-to-end-differentiable-physics-for-learning-and-control.pdf
2018
Cited alongside, same era.
M. Raissi, H. Babaee, and P. Givi, “Deep learning of turbulent scalar mixing,” Tech. Rep., 2018
2018
Cited alongside, same era.
D. Mrowca, C. Zhuang, E. Wang, N. Haber, L. Fei-Fei, J. B. Tenenbaum, and D. L. K. Yamins, “Flexible neural representation for physics prediction,” Advances in Neural Information Processing Systems , June 2018
2018
Cited alongside, same era.
T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” in Advances in Neural Information Processing Systems , 2018, pp. 6571–6583
J. Liang, M. Lin, and V. Koltun, “Differentiable cloth simulation for inverse problems,” in Advances in Neural Information Processing Systems , 2019, pp. 771–780
2019
Later among the works it cites.
P. C. Horak and J. C. Trinkle, “On the similarities and differences among contact models in robot simulation,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 493–499, 2019
2019
Later among the works it cites.
W. Jakob, “Enoki: structured vectorization and differentiation on modern processor architectures,” 2019, https://github.com/mitsuba-renderer/enoki
2019
Later among the works it cites.
J. Feng and N. Simon, “Sparse-input neural networks for high-dimensional nonparametric regression and classification,” 2019
2019
Later among the works it cites.
D. Kim, J. D. Carlo, B. Katz, G. Bledt, and S. Kim, “Highly dynamic quadruped locomotion via whole-body impulse control and model predictive control,” 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
R. T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” in Advances in Neural Information Processing Systems 31 , S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds. Curran Associates, Inc., 2018, pp. 6571–6583. [Online]. Available: http://papers.nips.cc/paper/7892-neural-ordinary-differential-equations.pdf
2018
Cited alongside, same era.
Z. Long, Y. Lu, X. Ma, and B. Dong, “Pde-net: Learning pdes from data,” in International Conference on Machine Learning , 2018, pp. 3208–3216
2018
Cited alongside, same era.
P. Brown and J. McPhee, “A 3d ellipsoidal volumetric foot–ground contact model for forward dynamics,” Multibody System Dynamics , vol. 42, no. 4, pp. 447–467, 2018
2018
Cited alongside, same era.
S. L. McCarty, L. M. Burke, and M. McGuire, “Parallel monotonic basin hopping for low thrust trajectory optimization,” in 2018 Space Flight Mechanics Meeting , 2018, p. 1452
2018
Cited alongside, same era.
F. Ramos, R. C. Possas, and D. Fox, “BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators,” Robotics: Science and Systems , 2019
2019
Cited alongside, same era.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, p. eaau5872, 2019
2019
Cited alongside, same era.
A. Zeng, S. Song, J. Lee, A. Rodriguez, and T. Funkhouser, “Tossingbot: Learning to throw arbitrary objects with residual physics,” 2019
2019
Cited alongside, same era.
Y. Li, J. Wu, R. Tedrake, J. B. Tenenbaum, and A. Torralba, “Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=rJgbSn09Ym
2019
Cited alongside, same era.
2019
Later among the works it cites.
E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” 2016–2020. [Online]. Available: http://pybullet.org
2020
Closest in time.
2020
Closest in time.
Y. Hu, L. Anderson, T.-M. Li, Q. Sun, N. Carr, J. Ragan-Kelley, and F. Durand, “DiffTaichi: Differentiable programming for physical simulation,” ICLR , 2020
2020
Closest in time.
M. Geilinger, D. Hahn, J. Zehnder, M. Bächer, B. Thomaszewski, and S. Coros, “ADD: Analytically differentiable dynamics for multi-body systems with frictional contact,” 2020
2020
Closest in time.
Y.-L. Qiao, J. Liang, V. Koltun, and M. C. Lin, “Scalable differentiable physics for learning and control,” in ICML , 2020
2020
Closest in time.
A. Sanchez-Gonzalez, J. Godwin, T. Pfaff, R. Ying, J. Leskovec, and P. W. Battaglia, “Learning to simulate complex physics with graph networks,” 2020
2020
Closest in time.
G. Sutanto, A. Wang, Y. Lin, M. Mukadam, G. Sukhatme, A. Rai, and F. Meier, “Encoding physical constraints in differentiable Newton-Euler algorithm,” ser. Proceedings of Machine Learning Research, A. M. Bayen, A. Jadbabaie, G. Pappas, P. A. Parrilo, B. Recht, C. Tomlin, and M. Zeilinger, Eds., vol. 120. The Cloud: PMLR, 10–11 Jun 2020, pp. 804–813. [Online]. Available: http://proceedings.mlr.press/v120/sutanto20a.html
2020
Closest in time.
M. Lutter, J. Silberbauer, J. Watson, and J. Peters, “Differentiable physics models for real-world offline model-based reinforcement learning,” 2020
2020
Closest in time.
E. Heiden, Z. Liu, R. K. Ramachandran, and G. S. Sukhatme, “Physics-based simulation of continuous-wave LIDAR for localization, calibration and tracking,” in International Conference on Robotics and Automation (ICRA) . IEEE, 2020
2020
Closest in time.
F. Biscani and D. Izzo, “A parallel global multiobjective framework for optimization: pagmo,” Journal of Open Source Software , vol. 5, no. 53, p. 2338, 2020. [Online]. Available: https://doi.org/10.21105/joss.02338
2020
Closest in time.
B. Bell, “CppAD: a package for C++ algorithmic differentiation,” http://www.coin-or.org/CppAD, 2020
2020
Closest in time.
B. Stellato, G. Banjac, P. Goulart, A. Bemporad, and S. Boyd, “OSQP: an operator splitting solver for quadratic programs,” Mathematical Programming Computation , vol. 12, no. 4, pp. 637–672, 2020. [Online]. Available: https://doi.org/10.1007/s12532-020-00179-2
2020
Closest in time.
X. B. Peng, E. Coumans, T. Zhang, T.-W. E. Lee, J. Tan, and S. Levine, “Learning agile robotic locomotion skills by imitating animals,” in Robotics: Science and Systems , 07 2020
2020
Closest in time.
2020
Closest in time.
J. K. Murthy, M. Macklin, F. Golemo, V. Voleti, L. Petrini, M. Weiss, B. Considine, J. Parent-Lévesque, K. Xie, K. Erleben, L. Paull, F. Shkurti, D. Nowrouzezahrai, and S. Fidler, “gradSim: Differentiable simulation for system identification and visuomotor control,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=c_E8kFWfhp0
2021
Closest in time.
Q. Lelidec, I. Kalevatykh, I. Laptev, C. Schmid, and J. Carpentier, “Differentiable simulation for physical system identification,” IEEE Robotics and Automation Letters , pp. 1–1, 2021
2021
Closest in time.