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Physical construction---the ability to compose objects, subject to physical dynamics, to serve some function---is fundamental to human intelligence.
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Logic-geometric programming: an optimization-based approach to combined task and motion planning
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
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Learning to poke by poking: Experiential learning of intuitive physics
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Learning visual predictive models of physics for playing billiards
Mastering the game of Go without human knowledge
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Fragkiadaki, K., Agrawal, P., Levine, S., and Malik, J · 2016
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Learning physical intuition of block towers by example
Lerer, A., Gross, S., and Fergus, R · 2016
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To fall or not to fall: A visual approach to physical stability prediction
Li, W., Azimi, S., Leonardis, A., and Fritz, M · 2016
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Newtonian image understanding: Unfolding the dynamics of objects in static images
Mottaghi, R., Bagherinezhad, H., Rastegari, M., and Farhadi, A · 2016
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Munos, R., Stepleton, T., Harutyunyan, A., and Bellemare, M · 2016
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Mastering the game of Go with deep neural networks and tree search
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Physics 101: Learning physical object properties from unlabeled videos
Wu, J., Lim, J. J., Zhang, H., Tenenbaum, J. B., and Freeman, W. T · 2016
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Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
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Shapestacks: Learning vision-based physical intuition for generalised object stacking
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Relational inductive bias for physical construction in humans and machines
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Unity: A general platform for intelligent agents
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Kool, W. and Welling, M · 2018
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Learning by playing solving sparse reward tasks from scratch
Riedmiller, M., Hafner, R., Lampe, T., Neunert, M., Degrave, J., van de Wiele, T., Mnih, V., Heess, N., and Springenberg, J. T · 2018
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Graph networks as learnable physics engines for inference and control
Sanchez-Gonzalez, A., Heess, N., Springenberg, J. T., Merel, J., Riedmiller, M., Hadsell, R., and Battaglia, P · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al · 2018
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Relational neural expectation maximization: unsupervised discovery of objects and their interactions
van Steenkiste, S., Chang, M., Greff, K., and Schmidhuber, J · 2018
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Nervenet: Learning structured policy with graph neural networks
Wang, T., Liao, R., Ba, J., and Fidler, S · 2018
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Composable planning with attributes
Zhang, A., Lerer, A., Sukhbaatar, S., Fergus, R., and Szlam, A · 2018
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Reasoning about physical interactions with object-oriented prediction and planning
Janner, M., Levine, S., Freeman, W. T., Tenenbaum, J. B., Finn, C., and Wu, J · 2019
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Deep reinforcement learning with relational inductive biases
Zambaldi, V., Raposo, D., Santoro, A., Bapst, V., Li, Y., Babuschkin, I., Tuyls, K., Reichert, D., Lillicrap, T., Lockhart, E., Shanahan, M., Langston, V., Pascanu, R., Botvinick, M., Vinyals, O., and Battaglia, P · 2019
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