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Sim-to-Real refers to the process of transferring policies learned in simulation to the real world, which is crucial for achieving practical robotics applications.
2015
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D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. , “Mastering the game of go with deep neural networks and tree search,” nature , vol. 529, no. 7587, pp. 484–489, 2016
2016
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Y. Deng, F. Bao, Y. Kong, Z. Ren, and Q. Dai, “Deep direct reinforcement learning for financial signal representation and trading,” IEEE transactions on neural networks and learning systems , vol. 28, no. 3, pp. 653–664, 2016
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2016
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E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” 2016
2016
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2017
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2017
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J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2017, pp. 23–30
2017
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V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare, J. Pineau, et al. , “An introduction to deep reinforcement learning,” Foundations and Trends® in Machine Learning , vol. 11, no. 3-4, pp. 219–354, 2018
2018
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K. Bousmalis, A. Irpan, P. Wohlhart, Y. Bai, M. Kelcey, M. Kalakrishnan, L. Downs, J. Ibarz, P. Pastor, K. Konolige, et al. , “Using simulation and domain adaptation to improve efficiency of deep robotic grasping,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 4243–4250
2018
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T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in International conference on machine learning . PMLR, 2018, pp. 1861–1870
2018
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2019
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W. Zhao, J. P. Queralta, L. Qingqing, and T. Westerlund, “Towards closing the sim-to-real gap in collaborative multi-robot deep reinforcement learning,” in 2020 5th International conference on robotics and automation engineering (ICRAE) . IEEE, 2020, pp. 7–12
2020
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2020
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K. Arndt, M. Hazara, A. Ghadirzadeh, and V. Kyrki, “Meta reinforcement learning for sim-to-real domain adaptation,” in 2020 IEEE international conference on robotics and automation (ICRA) . IEEE, 2020, pp. 2725–2731
2020
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B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in ECCV , 2020
2020
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2020
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S. Zhi, T. Laidlow, S. Leutenegger, and A. J. Davison, “In-place scene labelling and understanding with implicit scene representation,” in ICCV , 2021, pp. 15 838–15 847
2021
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2023
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X. Fang, D. Liu, P. Zhou, and G. Nan, “You can ground earlier than see: An effective and efficient pipeline for temporal sentence grounding in compressed videos,” in CVPR , 2023
2023
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S. Zhu, G. Wang, H. Blum, J. Liu, L. Song, M. Pollefeys, and H. Wang, “Sni-slam: Semantic neural implicit slam,” CVPR , 2024
2024
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2024
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K. Hsu, M. J. Kim, R. Rafailov, J. Wu, and C. Finn, “Vision-based manipulators need to also see from their hands,” in International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
Z. Zhu, S. Peng, V. Larsson, W. Xu, H. Bao, Z. Cui, M. R. Oswald, and M. Pollefeys, “Nice-slam: Neural implicit scalable encoding for slam,” in CVPR , 2022, pp. 12 786–12 796
2022
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Z. Yu, S. Peng, M. Niemeyer, T. Sattler, and A. Geiger, “Monosdf: Exploring monocular geometric cues for neural implicit surface reconstruction,” NeurIPS , pp. 25 018–25 032, 2022
2022
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M. Adamkiewicz, T. Chen, A. Caccavale, R. Gardner, P. Culbertson, J. Bohg, and M. Schwager, “Vision-only robot navigation in a neural radiance world,” RA-L , pp. 4606–4613, 2022
2022
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D. Driess, I. Schubert, P. Florence, Y. Li, and M. Toussaint, “Reinforcement learning with neural radiance fields,” NeurIPS , 2022
2022
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Y. Li, S. Li, V. Sitzmann, P. Agrawal, and A. Torralba, “3d neural scene representations for visuomotor control,” in CoRL , 2022, pp. 112–123
2022
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G. Wang, M. Xin, W. Wu, Z. Liu, and H. Wang, “Learning of long-horizon sparse-reward robotic manipulator tasks with base controllers,” IEEE Transactions on Neural Networks and Learning Systems , vol. 35, no. 3, pp. 4072–4081, 2022
2022
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Q. Dai, Y. Zhu, Y. Geng, C. Ruan, J. Zhang, and H. Wang, “Graspnerf: multiview-based 6-dof grasp detection for transparent and specular objects using generalizable nerf,” in ICRA , 2023, pp. 1757–1763
2023
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2024
Closest in time.
X. Fang, A. Easwaran, B. Genest, and P. N. Suganthan, “Your data is not perfect: Towards cross-domain out-of-distribution detection in class-imbalanced data,” ESWA , 2024
2024
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S. Zhu, G. Wang, H. Blum, J. Liu, L. Song, M. Pollefeys, and H. Wang, “Sni-slam: Semantic neural implicit slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 167–21 177
2024
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2024
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2024
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2024
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2024
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J. Gao, C. Gu, Y. Lin, Z. Li, H. Zhu, X. Cao, L. Zhang, and Y. Yao, “Relightable 3d gaussians: Realistic point cloud relighting with brdf decomposition and ray tracing,” in European Conference on Computer Vision . Springer, 2024, pp. 73–89
2024
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2024
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X. Fang, Z. Xiong, W. Fang, X. Qu, C. Chen, J. Dong, K. Tang, P. Zhou, Y. Cheng, and D. Liu, “Rethinking weakly-supervised video temporal grounding from a game perspective,” in ECCV , 2025
2025
Closest in time.