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We present Dojo, a differentiable physics engine for robotics that prioritizes stable simulation, accurate contact physics, and differentiability with respect to states, actions, and system parameters.
Fratelli Nistri, 1907
U. Dini, Lezioni di analisi infinitesimale · 1907
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.
M. H. Raibert, H. B. Brown Jr., M. Chepponis, J. Koechling, J. K. Hodgins, D. Dustman, W. K. Brennan, D. S. Barrett, C. M. Thompson, J. D. Hebert, W. Lee, and B. Lance, “Dynamically stable legged locomotion,” tech. rep., Massachusetts Institute of Technology Cambridge Artificial Intelligence Lab, 1989
1989
Earlier work this paper cites.
S. Mehrotra, “On the implementation of a primal-dual interior point method,” SIAM Journal on Optimization
1992
Earlier work this paper cites.
D. E. Stewart and J. C. Trinkle, “An implicit time-stepping scheme for rigid body dynamics with inelastic collisions and Coulomb friction,” International Journal for Numerical Methods in Engineering
1996
Earlier work this paper cites.
R. Grzeszczuk, D. Terzopoulos, and G. Hinton, “Neuroanimator: Fast neural network emulation and control of physics-based models,” in Proceedings of the 25th annual conference on Computer graphics and interactive techniques
1998
Earlier work this paper cites.
M. S. Lobo, L. Vandenberghe, S. Boyd, and H. Lebret, “Applications of second-order cone programming,” Linear Algebra and its Applications
1998
Earlier work this paper cites.
D. Murray-Smith, “The inverse simulation approach: a focused review of methods and applications,” Mathematics and computers in simulation
2000
Earlier work this paper cites.
J. E. Marsden and M. West, “Discrete mechanics and variational integrators,” Acta Numerica
2001
Earlier work this paper cites.
K. S. Bhat, C. D. Twigg, J. K. Hodgins, P. Khosla, Z. Popovic, and S. M. Seitz, “Estimating cloth simulation parameters from video,” 2003
2003
Earlier work this paper cites.
N. Koenig and A. Howard, “Design and use paradigms for Gazebo, an open-source multi-robot simulator,” in IEEE/RSJ International Conference on Intelligent Robots and Systems
2004
Earlier work this paper cites.
Cambridge University Press, 2004
S. Boyd and L. Vandenberghe, Convex Optimization · 2004
Earlier work this paper cites.
W. Li and E. Todorov, “Iterative linear quadratic regulator design for nonlinear biological movement systems,” in International Conference on Informatics in Control, Automation and Robotics
2004
Earlier work this paper cites.
C. K. Liu, A. Hertzmann, and Z. Popović, “Learning physics-based motion style with nonlinear inverse optimization,” ACM Transactions on Graphics (TOG)
2005
Earlier work this paper cites.
Springer, second ed., 2006
J. Nocedal and S. J. Wright, Numerical Optimization · 2006
Earlier work this paper cites.
M. A. Brubaker, L. Sigal, and D. J. Fleet, “Estimating contact dynamics,” in 2009 IEEE 12th International Conference on Computer Vision
2009
Earlier work this paper cites.
M. A. Brubaker, D. J. Fleet, and A. Hertzmann, “Physics-based person tracking using the anthropomorphic walker,” International journal of computer vision
2010
Earlier work this paper cites.
L. Vandenberghe, “The CVXOPT linear and quadratic cone program solvers,” Online: http://cvxopt.org/documentation/coneprog.pdf
2010
Earlier work this paper cites.
M. Salzmann and R. Urtasun, “Physically-based motion models for 3d tracking: A convex formulation,” in 2011 International Conference on Computer Vision
2011
Earlier work this paper cites.
J. J. Moreau, “On unilateral constraints, friction and plasticity,” in New Variational Techniques in Mathematical Physics
2011
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “MuJoCo: A physics engine for model-based control,” in IEEE/RSJ International Conference on Intelligent Robots and Systems
2012
Earlier work this paper cites.
Y. Tassa, T. Erez, and E. Todorov, “Synthesis and stabilization of complex behaviors through online trajectory optimization,” in IEEE/RSJ International Conference on Intelligent Robots and Systems
2012
Earlier work this paper cites.
Springer Science & Business Media, 2012
K. R. Kozlowski, Modelling and identification in robotics · 2012
Earlier work this paper cites.
M. Macklin, M. Müller, N. Chentanez, and T.-Y. Kim, “Unified particle physics for real-time applications,” ACM Transactions on Graphics (TOG)
2014
Earlier work this paper cites.
E. Todorov, “Convex and analytically-invertible dynamics with contacts and constraints: Theory and implementation in MuJoCo,” in IEEE International Conference on Robotics and Automation
2014
Earlier work this paper cites.
M. Posa, C. Cantu, and R. Tedrake, “A direct method for trajectory optimization of rigid bodies through contact,” The International Journal of Robotics Research
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Macklin, M. Müller, and N. Chentanez, “Xpbd: position-based simulation of compliant constrained dynamics,” in Proceedings of the 9th International Conference on Motion in Games
2016
Earlier work this paper cites.
Z. R. Manchester and M. A. Peck, “Quaternion variational integrators for spacecraft dynamics,” Journal of Guidance, Control, and Dynamics
2016
Earlier work this paper cites.
Y. Duan, X. Chen, R. Houthooft, J. Schulman, and P. Abbeel, “Benchmarking deep reinforcement learning for continuous control,” in International Conference on Machine Learning
2016
Earlier work this paper cites.
J. Wu, E. Lu, P. Kohli, B. Freeman, and J. Tenenbaum, “Learning to see physics via visual de-animation,” Advances in neural information processing systems
2017
Earlier work this paper cites.
P. M. Wensing, S. Kim, and J.-J. E. Slotine, “Linear matrix inequalities for physically consistent inertial parameter identification: A statistical perspective on the mass distribution,” IEEE Robotics and Automation Letters
2017
Cited alongside, same era.
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
2017
Cited alongside, same era.
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,” The International Journal of Robotics Research
2018
Cited alongside, same era.
P. Parmas, C. E. Rasmussen, J. Peters, and K. Doya, “Pipps: Flexible model-based policy search robust to the curse of chaos,” in International Conference on Machine Learning
2018
Cited alongside, same era.
J. Brüdigam and Z. Manchester, “Linear-time variational integrators in maximal coordinates,” in International Workshop on the Algorithmic Foundations of Robotics
2020
Later among the works it cites.
Z. Manchester and S. Kuindersma, “Variational contact-implicit trajectory optimization,” in Robotics Research
2020
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
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2018
Cited alongside, same era.
M. A. Toussaint, K. R. Allen, K. A. Smith, and J. B. Tenenbaum, “Differentiable physics and stable modes for tool-use and manipulation planning,” 2018
2018
Cited alongside, same era.
F. de Avila Belbute-Peres, K. Smith, K. Allen, J. Tenenbaum, and J. Z. Kolter, “End-to-end differentiable physics for learning and control,” Advances in neural information processing systems
2018
Cited alongside, same era.
C. Schenck and D. Fox, “Spnets: Differentiable fluid dynamics for deep neural networks,” in Conference on Robot Learning
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. Carpentier and N. Mansard, “Analytical derivatives of rigid body dynamics algorithms,” in Robotics: Science and systems (RSS 2018)
2018
Cited alongside, same era.
H. Mania, A. Guy, and B. Recht, “Simple random search of static linear policies is competitive for reinforcement learning,” in Advances in Neural Information Processing Systems
2018
Cited alongside, same era.
2019
Cited alongside, same era.
2021
Later among the works it cites.
E. Heiden, D. Millard, E. Coumans, Y. Sheng, and G. S. Sukhatme, “NeuralSim: Augmenting differentiable simulators with neural networks,” IEEE International Conference on Robotics and Automation
2021
Later among the works it cites.
M. Parmar, M. Halm, and M. Posa, “Fundamental challenges in deep learning for stiff contact dynamics,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
M. A. Z. Mora, M. Peychev, S. Ha, M. Vechev, and S. Coros, “Pods: Policy optimization via differentiable simulation,” in International Conference on Machine Learning
2021
Later among the works it cites.
Q. Le Lidec, I. Kalevatykh, I. Laptev, C. Schmid, and J. Carpentier, “Differentiable simulation for physical system identification,” IEEE Robotics and Automation Letters
2021
Later among the works it cites.
2021
Later among the works it cites.
B. E. Jackson, K. Tracy, and Z. Manchester, “Planning with attitude,” IEEE Robotics and Automation Letters
2021
Later among the works it cites.
S. Pfrommer, M. Halm, and M. Posa, “ContactNets: Learning discontinuous contact dynamics with smooth, implicit representations,” in Conference on Robot Learning
2021
Later among the works it cites.
H. J. Suh, M. Simchowitz, K. Zhang, and R. Tedrake, “Do differentiable simulators give better policy gradients?,” in International Conference on Machine Learning
2022
Closest in time.
A. M. Castro, F. N. Permenter, and X. Han, “An unconstrained convex formulation of compliant contact,” IEEE Transactions on Robotics
2022
Closest in time.
H. J. T. Suh, T. Pang, and R. Tedrake, “Bundled gradients through contact via randomized smoothing,” IEEE Robotics and Automation Letters
2022
Closest in time.
Nvidia, “PhysX physics engine,” 2022
2022
Closest in time.
G. Kim, D. Kang, J.-H. Kim, and H.-W. Park, “Contact-implicit differential dynamic programming for model predictive control with relaxed complementarity constraints,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2022
Closest in time.
M. Macklin, “Warp: A high-performance python framework for gpu simulation and graphics,” in NVIDIA GPU Technology Conference (GTC)
2022
Closest in time.
T. A. Howell, S. Le Cleac’h, S. Singh, P. Florence, Z. Manchester, and V. Sindhwani, “Trajectory optimization with optimization-based dynamics,” IEEE Robotics and Automation Letters
2022
Closest in time.
Q. Chen, S. Cheng, and N. Hovakimyan, “Simultaneous spatial and temporal assignment for fast uav trajectory optimization using bilevel optimization,” IEEE Robotics and Automation Letters
2023
Closest in time.
G. Authors, “Genesis: A universal and generative physics engine for robotics and beyond,” December 2024
2024
Closest in time.
2024
Closest in time.
R. Newbury, J. Collins, K. He, J. Pan, I. Posner, D. Howard, and A. Cosgun, “A review of differentiable simulators,” IEEE Access
2024
Closest in time.
S. Cheng, M. Kim, L. Song, C. Yang, Y. Jin, S. Wang, and N. Hovakimyan, “Difftune: Auto-tuning through auto-differentiation,” IEEE Transactions on Robotics
2024
Closest in time.
Accessed: 2025-02-19
NVIDIA Corporation, “Isaac Sim: Simulation for Robotics,” 2025 · 2025
Closest in time.
2025
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Accessed: 2025-02-17
Legged Robotics Group, “SimBenchmark: Benchmarking Simulation Performance for Legged Robots,” 2025 · 2025
Closest in time.