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Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically.
E. Coumans, “Bullet physics engine,” Open Source Software: http://bulletphysics. org , 2010
2010
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in IROS . IEEE, 2012, pp. 5026–5033
2012
Earlier work this paper cites.
A. Becker and T. Bretl, “Approximate steering of a unicycle under bounded model perturbation using ensemble control,” IEEE TRO , vol. 28, no. 3, pp. 580–591, 2012
2012
Earlier work this paper cites.
E. Coumans, “Bullet physics simulation,” in SIGGRAPH , 2015
2015
Earlier work this paper cites.
I. Mordatch, K. Lowrey, and E. Todorov, “Ensemble-cio: Full-body dynamic motion planning that transfers to physical humanoids,” in IROS , 2015
2015
Earlier work this paper cites.
I. Lenz, R. A. Knepper, and A. Saxena, “Deepmpc: Learning deep latent features for model predictive control,” in RSS , 2015
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in ICLR , 2015
2015
Earlier work this paper cites.
R. Kolbert, N. Chavan Dafle, and A. Rodriguez, “Experimental Validation of Contact Dynamics for In-Hand Manipulation,” in ISER , 2016
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 IROS . IEEE, 2016, pp. 30–37
2016
Earlier work this paper cites.
P. W. Battaglia, R. Pascanu, M. Lai, D. Rezende, and K. Kavukcuoglu, “Interaction networks for learning about objects, relations and physics,” in NeurIPS , 2016
2016
Earlier work this paper cites.
F. R. Hogan and A. Rodriguez, “Feedback control of the pusher-slider system: A story of hybrid and underactuated contact dynamics,” in WAFR , 2016
2016
Earlier work this paper cites.
J. Zhou, R. Paolini, A. Bagnell, and M. T. Mason, “A convex polynomial force-motion model for planar sliding: Identification and application,” in ICRA , 2016, pp. 372–377
2016
Earlier work this paper cites.
J. Degrave, M. Hermans, and J. Dambre, “A differentiable physics engine for deep learning in robotics,” in ICLR Workshop , 2016
2016
Cited alongside, same era.
S. Gu, T. Lillicrap, I. Sutskever, and S. Levine, “Continuous deep q-learning with model-based acceleration,” in ICML , 2016
2016
Cited alongside, same era.
N. Fazeli, S. Zapolsky, E. Drumwright, and A. Rodriguez, “Fundamental limitations in performance and interpretability of common planar rigid-body contact models,” in ISRR , 2017
2017
Cited alongside, same era.
N. Fazeli, S. Zapolsky, E. Drumwright, and A. Rodriguez, “Learning data-efficient rigid-body contact models: Case study of planar impact,” in CoRL , 2017, pp. 388–397
2017
Cited alongside, same era.
S. Racanière, T. Weber, D. Reichert, L. Buesing, A. Guez, D. J. Rezende, A. P. Badia, O. Vinyals, N. Heess, Y. Li, R. Pascanu, P. Battaglia, D. Silver, and D. Wierstra, “Imagination-augmented agents for deep reinforcement learning,” in NeurIPS , 2017
2017
Later among the works it cites.
J. B. Hamrick, A. J. Ballard, R. Pascanu, O. Vinyals, N. Heess, and P. W. Battaglia, “Metacontrol for adaptive imagination-based optimization,” in ICLR , 2017
2017
Later among the works it cites.
2017
Later among the works it 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 IROS , 2018
2018
Later among the works it cites.
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2017
Cited alongside, same era.
M. B. Chang, T. Ullman, A. Torralba, and J. B. Tenenbaum, “A compositional object-based approach to learning physical dynamics,” in ICLR , 2017
2017
Cited alongside, same era.
A. Byravan and D. Fox, “Se3-nets: Learning rigid body motion using deep neural networks,” in ICRA , 2017
2017
Cited alongside, same era.
J. Zhou, A. Bagnell, and M. T. Mason, “A fast stochastic contact model for planar pushing and grasping: Theory and experimental validation,” in RSS , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
D. Silver, H. van Hasselt, M. Hessel, T. Schaul, A. Guez, T. Harley, G. Dulac-Arnold, D. Reichert, N. Rabinowitz, A. Barreto, and T. Degris, “The predictron: End-to-end learning and planning,” in ICML , 2017
2017
Cited alongside, same era.
J. Oh, S. Singh, and H. Lee, “Value prediction network,” in NeurIPS , 2017
2017
Cited alongside, same era.
K. Chatzilygeroudis and J.-B. Mouret, “Using parameterized black-box priors to scale up model-based policy search for robotics,” in ICRA , 2018
2018
Later among the works it cites.
M. Toussaint, K. Allen, K. Smith, and J. Tenenbaum, “Differentiable physics and stable modes for tool-use and manipulation planning,” in RSS , 2018
2018
Later among the works it cites.
A. Nagabandi, G. Kahn, R. S. Fearing, and S. Levine, “Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning,” in ICRA , 2018
2018
Later among the works it cites.
G. Farquhar, T. Rocktäschel, M. Igl, and S. Whiteson, “Treeqn and atreec: Differentiable tree planning for deep reinforcement learning,” in ICLR , 2018
2018
Later among the works it cites.
A. Srinivas, A. Jabri, P. Abbeel, S. Levine, and C. Finn, “Universal planning networks,” in ICML , 2018
2018
Later among the works it cites.
M. Bauza, F. R. Hogan, and A. Rodriguez, “A data-efficient approach to precise and controlled pushing,” in CoRL , 2018
2018
Later among the works it cites.
A. Sanchez-Gonzalez, N. Heess, J. T. Springenberg, J. Merel, M. Riedmiller, R. Hadsell, and P. Battaglia, “Graph networks as learnable physics engines for inference and control,” in ICML , 2018
2018
Later among the works it cites.