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The ability to predict future outcomes given control actions is fundamental for physical reasoning.
Generating diverse high-fidelity images with vq-vae-2, 2019
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D4rl: Datasets for deep data-driven reinforcement learning, 2021
Fu, J., Kumar, A., Nachum, O., Tucker, G., and Levine, S · 2004
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Planning to explore via self-supervised world models, 2020
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A generalized iterative lqg method for locally-optimal feedback control of constrained nonlinear stochastic systems
Todorov, E. and Li, W · 2005
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An image is worth 16x16 words: Transformers for image recognition at scale, 2021
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2010
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Mastering atari with discrete world models, 2022
Hafner, D., Lillicrap, T., Norouzi, M., and Ba, J · 2010
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An overview of model predictive control
Holkar, K. and Waghmare, L. M · 2010
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Pilco: A model-based and data-efficient approach to policy search
Deisenroth, M. P. and Rasmussen, C. E · 2011
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Deepmpc: Learning deep latent features for model predictive control
Lenz, I., Knepper, R. A., and Saxena, A · 2015
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Imagenet large scale visual recognition challenge, 2015
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Embed to control: A locally linear latent dynamics model for control from raw images, 2015
Watter, M., Springenberg, J. T., Boedecker, J., and Riedmiller, M · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Deep visual foresight for planning robot motion, 2017
Finn, C. and Levine, S · 2017
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Information theoretic mpc for model-based reinforcement learning
Williams, G., Wagener, N., Goldfain, B., Drews, P., Rehg, J. M., Boots, B., and Theodorou, E. A · 2017
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models, 2018
Chua, K., Calandra, R., McAllister, R., and Levine, S · 2018
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Visual foresight: Model-based deep reinforcement learning for vision-based robotic control, 2018
Ebert, F., Finn, C., Dasari, S., Xie, A., Lee, A., and Levine, S · 2018
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World models
Ha, D. and Schmidhuber, J · 2018
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Zero-shot visual imitation, 2018
Pathak, D., Mahmoudieh, P., Luo, G., Agrawal, P., Chen, D., Shentu, Y., Shelhamer, E., Malik, J., Efros, A. A., and Darrell, T · 2018
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Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., de Las Casas, D., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., Lillicrap, T., and Riedmiller, M · 2018
Transformer-based world models are happy with 100k interactions, 2023
Robine, J., Höftmann, M., Uelwer, T., and Harmeling, S · 2023
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Manipulate by seeing: Creating manipulation controllers from pre-trained representations, 2023
Wang, J., Dasari, S., Srirama, M. K., Tulsiani, S., and Gupta, A · 2023
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Learning interactive real-world simulators, 2023
Yang, M., Du, Y., Ghasemipour, K., Tompson, J., Schuurmans, D., and Abbeel, P · 2023
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Learning fine-grained bimanual manipulation with low-cost hardware, 2023
Zhao, T. Z., Kumar, V., Levine, S., and Finn, C · 2023
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Train offline, test online: A real robot learning benchmark, 2023
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Learning latent dynamics for planning from pixels, 2019
Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., and Davidson, J · 2019
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Emerging properties in self-supervised vision transformers, 2021
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
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Discovering and achieving goals via world models, 2021
Mendonca, R., Rybkin, O., Daniilidis, K., Hafner, D., and Pathak, D · 2021
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Learning predictive representations for deformable objects using contrastive estimation
Yan, W., Vangipuram, A., Abbeel, P., and Pinto, L · 2021
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Legged locomotion in challenging terrains using egocentric vision, 2022
Agarwal, A., Kumar, A., Malik, J., and Pathak, D · 2022
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Temporal difference learning for model predictive control, 2022
Hansen, N., Wang, X., and Su, H · 2022
Cited alongside, same era.
Zhou, G., Dean, V., Srirama, M. K., Rajeswaran, A., Pari, J., Hatch, K., Jain, A., Yu, T., Abbeel, P., Pinto, L., Finn, C., and Gupta, A · 2023
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V-JEPA: Latent video prediction for visual representation learning, 2024
Bardes, A., Garrido, Q., Ponce, J., Chen, X., Rabbat, M., LeCun, Y., Assran, M., and Ballas, N · 2024
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Genie: Generative interactive environments, 2024
Bruce, J., Dennis, M., Edwards, A., Parker-Holder, J., Shi, Y., Hughes, E., Lai, M., Mavalankar, A., Steigerwald, R., Apps, C., Aytar, Y., Bechtle, S., Behbahani, F., Chan, S., Heess, N., Gonzalez, L., Osindero, S., Ozair, S., Reed, S., Zhang, J., Zolna, K., Clune, J., de Freitas, N., Singh, S., and Rocktäschel, T · 2024
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Diffusion policy: Visuomotor policy learning via action diffusion, 2024
Chi, C., Xu, Z., Feng, S., Cousineau, E., Du, Y., Burchfiel, B., Tedrake, R., and Song, S · 2024
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Ding, Z., Zhang, A., Tian, Y., and Zheng, Q · 2024
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Robot utility models: General policies for zero-shot deployment in new environments
Etukuru, H., Naka, N., Hu, Z., Lee, S., Mehu, J., Edsinger, A., Paxton, C., Chintala, S., Pinto, L., and Shafiullah, N. M. M · 2024
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Mastering diverse domains through world models, 2024
Hafner, D., Pasukonis, J., Ba, J., and Lillicrap, T · 2024
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Baku: An efficient transformer for multi-task policy learning, 2024
Haldar, S., Peng, Z., and Pinto, L · 2024
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Td-mpc2: Scalable, robust world models for continuous control, 2024
Hansen, N., Su, H., and Wang, X · 2024
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Chain-of-thought predictive control, 2024
Jia, Z., Thumuluri, V., Liu, F., Chen, L., Huang, Z., and Su, H · 2024
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Behavior generation with latent actions, 2024
Lee, S., Wang, Y., Etukuru, H., Kim, H. J., Shafiullah, N. M. M., and Pinto, L · 2024
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Liu, Y., Zhang, K., Li, Y., Yan, Z., Gao, C., Chen, R., Yuan, Z., Huang, Y., Sun, H., Gao, J., He, L., and Sun, L · 2024
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Eureka: Human-level reward design via coding large language models, 2024
Ma, Y. J., Liang, W., Wang, G., Huang, D.-A., Bastani, O., Jayaraman, D., Zhu, Y., Fan, L., and Anandkumar, A · 2024
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Dinov2: Learning robust visual features without supervision, 2024
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.-Y., Li, S.-W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., and Bojanowski, P · 2024
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Any-point trajectory modeling for policy learning, 2024
Wen, C., Lin, X., So, J., Chen, K., Dou, Q., Gao, Y., and Abbeel, P · 2024
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Adaptigraph: Material-adaptive graph-based neural dynamics for robotic manipulation, 2024
Zhang, K., Li, B., Hauser, K., and Li, Y · 2024
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