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Effectively performing object rearrangement is an essential skill for mobile manipulators, e.g., setting up a dinner table or organizing a desk.
E. R. Vieira, D. Nakhimovich, K. Gao, R. Wang, J. Yu, and K. E. Bekris, “Persistent homology for effective non-prehensile manipulation,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 1918–1924
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K. Gao, D. Lau, B. Huang, K. E. Bekris, and J. Yu, “Fast high-quality tabletop rearrangement in bounded workspace,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 1961–1967
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S. D. Han, N. M. Stiffler, A. Krontiris, K. E. Bekris, and J. Yu, “Complexity results and fast methods for optimal tabletop rearrangement with overhand grasps,” The International Journal of Robotics Research , vol. 37, no. 13-14, pp. 1775–1795, 2018
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W. Yuan, K. Hang, D. Kragic, M. Y. Wang, and J. A. Stork, “End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer,” Robotics and Autonomous Systems , vol. 119, pp. 119–134, 2019
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Y. Labbé, S. Zagoruyko, I. Kalevatykh, I. Laptev, J. Carpentier, M. Aubry, and J. Sivic, “Monte-carlo tree search for efficient visually guided rearrangement planning,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3715–3722, 2020
2020
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H. Song, J. A. Haustein, W. Yuan, K. Hang, M. Y. Wang, D. Kragic, and J. A. Stork, “Multi-object rearrangement with monte carlo tree search: A case study on planar nonprehensile sorting,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 9433–9440
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S. H. Cheong, B. Y. Cho, J. Lee, C. Kim, and C. Nam, “Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 7791–7797
2020
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K. Gao and J. Yu, “Toward efficient task planning for dual-arm tabletop object rearrangement,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 10 425–10 431
2022
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R. Wang, K. Gao, J. Yu, and K. Bekris, “Lazy rearrangement planning in confined spaces,” in Proceedings of the International Conference on Automated Planning and Scheduling , vol. 32, 2022, pp. 385–393
2022
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H. Zhang, C. Wu, Z. Zhang, Y. Zhu, H. Lin, Z. Zhang, Y. Sun, T. He, J. Mueller, R. Manmatha, et al. , “Resnest: Split-attention networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 2736–2746
2022
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K. Gao, S. W. Feng, B. Huang, and J. Yu, “Minimizing running buffers for tabletop object rearrangement: Complexity, fast algorithms, and applications,” The International Journal of Robotics Research , p. 02783649231178565, 2023
2023
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2020
Cited alongside, same era.
A. Zeng, P. Florence, J. Tompson, S. Welker, J. Chien, M. Attarian, T. Armstrong, I. Krasin, D. Duong, V. Sindhwani, et al. , “Transporter networks: Rearranging the visual world for robotic manipulation,” in Conference on Robot Learning . PMLR, 2021, pp. 726–747
2021
Cited alongside, same era.
B. Huang, S. D. Han, A. Boularias, and J. Yu, “Dipn: Deep interaction prediction network with application to clutter removal,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 4694–4701
2021
Cited alongside, same era.
B. Huang, S. D. Han, J. Yu, and A. Boularias, “Visual foresight trees for object retrieval from clutter with nonprehensile rearrangement,” IEEE Robotics and Automation Letters , vol. 7, no. 1, pp. 231–238, 2021
2021
Cited alongside, same era.
R. Wang, K. Gao, D. Nakhimovich, J. Yu, and K. E. Bekris, “Uniform object rearrangement: From complete monotone primitives to efficient non-monotone informed search,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 6621–6627
2021
Cited alongside, same era.
2021
Cited alongside, same era.
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2023
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2023
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B. Tang and G. S. Sukhatme, “Selective object rearrangement in clutter,” in Conference on Robot Learning . PMLR, 2023, pp. 1001–1010
2023
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2023
Closest in time.