[19] C. Finn, I. J. Goodfellow, and S. Levine. Unsupervised learning for physical interaction through video prediction. CoRR, abs/1605.07157, 2016.
[20] C. Finn and S. Levine. Deep visual foresight for planning robot motion. In 2017 IEEE International Conference on Robotics and Automation (ICRA), pages 2786-2793. IEEE, 2017.
[21] K. Fragkiadaki, P. Agrawal, S. Levine, and J. Malik. Learning visual predictive models of physics for playing billiards. In Y. Bengio and Y. LeCun, editors, 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, 2016.
[22] R. Girdhar, L. Gustafson, A. Adcock, and L. van der Maaten. Forward prediction for physical reasoning. CoRR, abs/2006.10734, 2020.
[23] D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi. Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603, 2019.
[24] D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson. Learning latent dynamics for planning from pixels. In International conference on machine learning, pages 2555-2565. PMLR, 2019.
[25] D. Hafner, T. P. Lillicrap, J. Ba, and M. Norouzi. Dream to control: Learning behaviors by latent imagination. CoRR, abs/1912.01603, 2019.
[26] M. Janner, J. Fu, M. Zhang, and S. Levine. When to trust your model: Model-based policy optimization. CoRR, abs/1906.08253, 2019.
[27] M. Janner, S. Levine, W. T. Freeman, J. B. Tenenbaum, C. Finn, and J. Wu. Reasoning about physical interactions with object-oriented prediction and planning. In International Conference on Learning Representations, 2019.
[28] T. Kipf, E. van der Pol, and M. Welling. Contrastive learning of structured world models. arXiv preprint arXiv:1911.12247, 2019.
[29] A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al. Segment anything. arXiv preprint arXiv:2304.02643, 2023.
[30] A. X. Lee, R. Zhang, F. Ebert, P. Abbeel, C. Finn, and S. Levine. Stochastic adversarial video prediction. CoRR, abs/1804.01523, 2018.
[31] A. Lerer, S. Gross, and R. Fergus. Learning physical intuition of block towers by example. CoRR, abs/1603.01312, 2016.
[32] T. Li, M. Slavcheva, M. Zollhoefer, S. Green, C. Lassner, C. Kim, T. Schmidt, S. Lovegrove, M. Goesele, and Z. Lv. Neural 3d video synthesis. arXiv preprint arXiv:2103.02597, 2021.
[33] W. Li, S. Azimi, A. Leonardis, and M. Fritz. To fall or not to fall: A visual approach to physical stability prediction. CoRR, abs/1604.00066, 2016.
[34] Y. Li, S. Li, V. Sitzmann, P. Agrawal, and A. Torralba. 3d neural scene representations for visuomotor control. arXiv preprint arXiv:2107.04004, 2021.
[35] Y. Li, T. Lin, K. Yi, D. Bear, D. L. Yamins, J. Wu, J. B. Tenenbaum, and A. Torralba. Visual grounding of learned physical models. In International Conference on Machine Learning, 2020.
[36] Y. Li, J. Wu, R. Tedrake, J. B. Tenenbaum, and A. Torralba. Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids. In ICLR, 2019.
[37] Y. Li, J. Wu, J.-Y. Zhu, J. B. Tenenbaum, A. Torralba, and R. Tedrake. Propagation networks for model-based control under partial observation. In 2019 International Conference on Robotics and Automation (ICRA), pages 1205-1211. IEEE, 2019.
[38] X. Lin, Y. Wang, Z. Huang, and D. Held. Learning visible connectivity dynamics for cloth smoothing. In Conference on Robot Learning, 2021.