Fetching the paper…
Reading the bibliography…
In recent years, an increasing amount of work has focused on differentiable physics simulation and has produced a set of open source projects such as Tiny Differentiable Simulator, Nimble Physics, diffTaichi, Brax, Warp, Dojo and DiffCoSim.
Formulating dynamic multi-rigid-body contact problems with friction as solvable linear complementarity problems
Anitescu, M. and Potra, F. A · 1997
Earlier work this paper cites.
Calculus of variations
Gelfand, I. M., Silverman, R. A., et al · 2000
Earlier work this paper cites.
Position based dynamics
Müller, M., Heidelberger, B., Hennix, M., and Ratcliff, J · 2007
Earlier work this paper cites.
A convex, smooth and invertible contact model for trajectory optimization
Todorov, E · 2011
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
Earlier work this paper cites.
Projective dynamics: Fusing constraint projections for fast simulation
Bouaziz, S., Martin, S., Liu, T., Kavan, L., and Pauly, M · 2014
Earlier work this paper cites.
Convex and analytically-invertible dynamics with contacts and constraints: Theory and implementation in MuJoCo
Todorov, E · 2014
Earlier work this paper cites.
Xpbd: position-based simulation of compliant constrained dynamics
Macklin, M., Müller, M., and Chentanez, N · 2016
Earlier work this paper cites.
Optnet: Differentiable optimization as a layer in neural networks
Amos, B. and Kolter, J. Z · 2017
Earlier work this paper cites.
Modeling, sensitivity analysis, and optimization of hybrid, constrained mechanical systems
Corner, S. M · 2017
Earlier work this paper cites.
Automatic differentiation of rigid body dynamics for optimal control and estimation
Giftthaler, M., Neunert, M., Stäuble, M., Frigerio, M., Semini, C., and Buchli, J · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
Analytical derivatives of rigid body dynamics algorithms
Carpentier, J. and Mansard, N · 2018
Earlier work this paper cites.
End-to-end differentiable physics for learning and control
de Avila Belbute-Peres, F., Smith, K., Allen, K., Tenenbaum, J., and Kolter, J. Z · 2018
Earlier work this paper cites.
Dart: Dynamic animation and robotics toolkit
Lee, J., Grey, M. X., Ha, S., Kunz, T., Jain, S., Ye, Y., Srinivasa, S. S., Stilman, M., and Liu, C. K · 2018
Cited alongside, same era.
Differentiable convex optimization layers
Agrawal, A., Amos, B., Barratt, S., Boyd, S., Diamond, S., and Kolter, J. Z · 2019
Cited alongside, same era.
A differentiable physics engine for deep learning in robotics
Degrave, J., Hermans, M., Dambre, J., et al · 2019
Cited alongside, same era.
Differentiable cloth simulation for inverse problems
Liang, J., Lin, M., and Koltun, V · 2019
Cited alongside, same era.
Add: Analytically differentiable dynamics for multi-body systems with frictional contact
Geilinger, M., Hahn, D., Zehnder, J., Bächer, M., Thomaszewski, B., and Coros, S · 2020
Cited alongside, same era.
Difftaichi: Differentiable programming for physical simulation
Hu, Y., Anderson, L., Li, T.-M., Sun, Q., Carr, N., Ragan-Kelley, J., and Durand, F · 2020
Neuralsim: Augmenting differentiable simulators with neural networks
Heiden, E., Millard, D., Coumans, E., Sheng, Y., and Sukhatme, G. S · 2021
Later among the works it cites.
Differentiable simulation for physical system identification
Le Lidec, Q., Kalevatykh, I., Laptev, I., Schmid, C., and Carpentier, J · 2021
Later among the works it cites.
Diffcloth: Differentiable cloth simulation with dry frictional contact
Li, Y., Du, T., Wu, K., Xu, J., and Matusik, W · 2021
Later among the works it cites.
gradsim: Differentiable simulation for system identification and visuomotor control
Murthy, J. K., Macklin, M., Golemo, F., Voleti, V., Petrini, L., Weiss, M., Considine, B., Parent-Lévesque, J., Xie, K., Erleben, K., Paull, L., Shkurti, F., Nowrouzezahrai, D., and Fidler, S · 2021
Later among the works it cites.
Differentiable implicit soft-body physics
Rojas, J., Sifakis, E., and Kavan, L · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Incremental potential contact: Intersection-and inversion-free, large-deformation dynamics
Li, M., Ferguson, Z., Schneider, T., Langlois, T., Zorin, D., Panozzo, D., Jiang, C., and Kaufman, D. M · 2020
Cited alongside, same era.
Differentiable Physics Simulation
Liang, J. and Lin, M. C · 2020
Cited alongside, same era.
Primal/dual descent methods for dynamics
Macklin, M., Erleben, K., Müller, M., Chentanez, N., Jeschke, S., and Kim, T · 2020
Cited alongside, same era.
Scalable differentiable physics for learning and control
Qiao, Y.-L., Liang, J., Koltun, V., and Lin, M · 2020
Cited alongside, same era.
Encoding physical constraints in differentiable newton-euler algorithm
Sutanto, G., Wang, A., Lin, Y., Mukadam, M., Sukhatme, G., Rai, A., and Meier, F · 2020
Cited alongside, same era.
Learning physical constraints with neural projections
Yang, S., He, X., and Zhu, B · 2020
Cited alongside, same era.
Later among the works it cites.
Diffsdfsim: Differentiable rigid-body dynamics with implicit shapes
Strecke, M. and Stueckler, J · 2021
Later among the works it cites.
Fast and feature-complete differentiable physics for articulated rigid bodies with contact
Werling, K., Omens, D., Lee, J., Exarchos, I., and Liu, C. K · 2021
Later among the works it cites.
An End-to-End Differentiable Framework for Contact-Aware Robot Design
Xu, J., Chen, T., Zlokapa, L., Foshey, M., Matusik, W., Sueda, S., and Agrawal, P · 2021
Later among the works it cites.
Extending lagrangian and hamiltonian neural networks with differentiable contact models
Zhong, Y. D., Dey, B., and Chakraborty, A · 2021
Later among the works it cites.
Dojo: A differentiable simulator for robotics
Howell, T. A., Cleac’h, S. L., Kolter, J. Z., Schwager, M., and Manchester, Z · 2022
Closest in time.
Solving optimal control of rigid-body dynamics with collisions using the hybrid minimum principle
Hu, W., Long, J., Zang, Y., E, W., and Han, J · 2022
Closest in time.
Warp: A high-performance python framework for gpu simulation and graphics
Macklin, M · 2022
Closest in time.
Do differentiable simulators give better policy gradients?
Suh, H., Simchowitz, M., Zhang, K., and Tedrake, R · 2022
Closest in time.
Accelerated policy learning with parallel differentiable simulation
Xu, J., Macklin, M., Makoviychuk, V., Narang, Y., Garg, A., Ramos, F., and Matusik, W · 2022
Closest in time.