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Recent years in robotics and imitation learning have shown remarkable progress in training large-scale foundation models by leveraging data across a multitude of embodiments.
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A. Mandlekar, J. Booher, M. Spero, A. Tung, A. Gupta, Y. Zhu, A. Garg, S. Savarese, and L. Fei-Fei, “Scaling robot supervision to hundreds of hours with roboturk: Robotic manipulation dataset through human reasoning and dexterity,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 1048–1055
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Y.-H. H. Tsai, V. Dhar, J. Li, B. Zhang, and J. Zhang, “Multimodal large language model for visual navigation,” 2023
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K. Kang, G. Kahn, and S. Levine, “Hierarchically integrated models: Learning to navigate from heterogeneous robots,” in Annual Conference on Robot Learning (CoRL) , 2021
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S. Ramos, S. Girgin, L. Hussenot, D. Vincent, H. Yakubovich, D. Toyama, A. Gergely, P. Stanczyk, R. Marinier, J. Harmsen, O. Pietquin, and N. Momchev, “Rlds: an ecosystem to generate, share and use datasets in reinforcement learning,” 2021
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R. Bommasani et al. , “On the opportunities and risks of foundation models,” 2022
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H. You, T. Yang, Y. Zheng, J. Hao, and E. Taylor, Matthew, “Cross-domain adaptive transfer reinforcement learning based on state-action correspondence,” in Uncertainty in Artificial Intelligence , 2022
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J. H. Yang, D. Sadigh, and C. Finn, “Polybot: Training one policy across robots while embracing variability,” in Annual Conference on Robot Learning (CoRL) , 2023
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N. Hirose, D. Shah, A. Sridhar, and S. Levine, “Exaug: Robot-conditioned navigation policies via geometric experience augmentation,” in International Conference on Robotics and Automation (ICRA) , 2023
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S. Bahl, R. Mendonca, L. Chen, U. Jain, and D. Pathak, “Affordances from human videos as a versatile representation for robotics,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 01–13
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D. Shah, A. Sridhar, N. Dashora, K. Stachowicz, K. Black, N. Hirose, and S. Levine, “ViNT: A foundation model for visual navigation,” in Annual Conference on Robot Learning (CoRL) , 2023
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K. Bousmalis et al. , “Robocat: A self-improving foundation agent for robotic manipulation,” ArXiv , 2023
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A. Hu, L. Russell, H. Yeo, Z. Murez, G. Fedoseev, A. Kendall, J. Shotton, and G. Corrado, “Gaia-1: A generative world model for autonomous driving,” 2023
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S. Dass, J. Yapeter, J. Zhang, J. Zhang, K. Pertsch, S. Nikolaidis, and J. J. Lim, “Clvr jaco play dataset,” 2023. [Online]. Available: https://github.com/clvrai/clvr_jaco_play_dataset
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G. Zhou, V. Dean, M. K. Srirama, A. Rajeswaran, J. Pari, K. Hatch, A. Jain, T. Yu, P. Abbeel, L. Pinto, C. Finn, and A. Gupta, “Train offline, test online: A real robot learning benchmark,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023
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C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” in Proceedings of Robotics: Science and Systems (RSS) , 2023
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A. Sridhar, D. Shah, C. Glossop, and S. Levine, “Nomad: Goal masked diffusion policies for navigation and exploration,” 2023
2023
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M. Yang, Y. Du, K. Ghasemipour, J. Tompson, L. Kaelbling, D. Schuurmans, and P. Abbeel, “Learning interactive real-world simulators,” 2024
2024
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N. Hirose, D. Shah, A. Sridhar, and S. Levine, “Sacson: Scalable autonomous control for social navigation,” IEEE Robotics and Automation Letters , 2024
2024
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Z. Fu, T. Z. Zhao, and C. Finn, “Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation,” 2024
2024
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