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Autonomous robot exploration requires a robot to efficiently explore and map unknown environments.
Y. Cao, Y. Wang, A. Vashisth, H. Fan, and G. A. Sartoretti, “Catnipp: Context-aware attention-based network for informative path planning,” in Conference on Robot Learning . PMLR, 2023, pp. 1928–1937
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D. Zhu, T. Li, D. Ho, C. Wang, and M. Q.-H. Meng, “Deep reinforcement learning supervised autonomous exploration in office environments,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 7548–7555
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2020
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2021
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H. Zhu, C. Cao, Y. Xia, S. Scherer, J. Zhang, and W. Wang, “Dsvp: Dual-stage viewpoint planner for rapid exploration by dynamic expansion,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 7623–7630
2021
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Cited in the paper.
Y. Cao, T. Hou, Y. Wang, X. Yi, and G. Sartoretti, “Ariadne: A reinforcement learning approach using attention-based deep networks for exploration,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 10 219–10 225
2023
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J. Huang, B. Zhou, Z. Fan, Y. Zhu, Y. Jie, L. Li, and H. Cheng, “Fael: fast autonomous exploration for large-scale environments with a mobile robot,” IEEE Robotics and Automation Letters , vol. 8, no. 3, pp. 1667–1674, 2023
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
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Cao, R. Zhao, Y. Wang, B. Xiang, and G. Sartoretti, “Deep reinforcement learning-based large-scale robot exploration,” IEEE Robotics and Automation Letters , 2024
2024
Closest in time.
C. Chi, Z. Xu, S. Feng, E. Cousineau, Y. Du, B. Burchfiel, R. Tedrake, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” The International Journal of Robotics Research , 2024
2024
Closest in time.