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Mobile manipulation constitutes a fundamental task for robotic assistants and garners significant attention within the robotics community.
K. C. Vivaldini, J. P. Galdames, T. S. Bueno, R. C. Araújo, R. M. Sobral, M. Becker, and G. A. Caurin, “Robotic forklifts for intelligent warehouses: Routing, path planning, and auto-localization,” in 2010 IEEE International Conference on Industrial Technology . IEEE, 2010, pp. 1463–1468
2010
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P. Hebert, M. Bajracharya, J. Ma, N. Hudson, A. Aydemir, J. Reid, C. Bergh, J. Borders, M. Frost, M. Hagman et al. , “Mobile manipulation and mobility as manipulation—design and algorithms of robosimian,” Journal of Field Robotics , vol. 32, no. 2, pp. 255–274, 2015
2015
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B. Çalli, A. Singh, A. Walsman, S. S. Srinivasa, P. Abbeel, and A. M. Dollar, “The ycb object and model set: Towards common benchmarks for manipulation research,” 2015 International Conference on Advanced Robotics (ICAR) , pp. 510–517, 2015
2015
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O. Brock, J. Park, and M. Toussaint, “Mobility and manipulation,” Springer Handbook of Robotics , pp. 1007–1036, 2016
2016
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K. Xu, Y. Shi, L. Zheng, J. Zhang, M. Liu, H. Huang, H. Su, D. Cohen-Or, and B. Chen, “3d attention-driven depth acquisition for object identification,” ACM Transactions on Graphics (TOG) , vol. 35, no. 6, pp. 1–14, 2016
2016
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C. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 77–85, 2016
2016
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2017
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2018
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Y. Zhou, C. Barnes, J. Lu, J. Yang, and H. Li, “On the continuity of rotation representations in neural networks,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 5738–5746, 2018
2018
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J. Mahler, M. Matl, V. Satish, M. Danielczuk, B. DeRose, S. McKinley, and K. Goldberg, “Learning ambidextrous robot grasping policies,” Science Robotics , vol. 4, no. 26, p. eaau4984, 2019
2019
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L. Zheng, C. Zhu, J. Zhang, H. Zhao, H. Huang, M. Niessner, and K. Xu, “Active scene understanding via online semantic reconstruction,” in Computer Graphics Forum , vol. 38, no. 7. Wiley Online Library, 2019, pp. 103–114
2019
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S. Patki, E. Fahnestock, T. M. Howard, and M. R. Walter, “Language-guided semantic mapping and mobile manipulation in partially observable environments,” in Conference on Robot Learning . PMLR, 2020, pp. 1201–1210
2020
Earlier work this paper cites.
C. Wang, Q. Zhang, Q. Tian, S. Li, X. Wang, D. Lane, Y. Petillot, and S. Wang, “Learning mobile manipulation through deep reinforcement learning,” Sensors , vol. 20, no. 3, p. 939, 2020
2020
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C. R. Garrett, C. Paxton, T. Lozano-Pérez, L. P. Kaelbling, and D. Fox, “Online replanning in belief space for partially observable task and motion problems,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 5678–5684
2020
Cited alongside, same era.
H. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 11 441–11 450, 2020
2020
Cited alongside, same era.
J. Zhang, C. Zhu, L. Zheng, and K. Xu, “Fusion-aware point convolution for online semantic 3d scene segmentation,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 4533–4542, 2020
2020
Cited alongside, same era.
C. Wang, H. Fang, M. Gou, H. Fang, J. Gao, C. Lu, and S. J. Tong, “Graspness discovery in clutters for fast and accurate grasp detection,” 2021 IEEE/CVF International Conference on Computer Vision (ICCV) , pp. 15 944–15 953, 2021
2021
K. He, R. Newbury, T. Tran, J. Haviland, B. Burgess-Limerick, D. Kulić, P. Corke, and A. Cosgun, “Visibility maximization controller for robotic manipulation,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 8479–8486, 2022
2022
Later among the works it cites.
K. Yamazaki, S. Suzuki, and Y. Kuribayashi, “Approaching motion planning for mobile manipulators considering the uncertainty of self-positioning and object’s pose estimation,” Robotics and Autonomous Systems , vol. 158, p. 104232, 2022
2022
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2022
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2022
Later among the works it cites.
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Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
A. Heins, M. Jakob, and A. P. Schoellig, “Mobile manipulation in unknown environments with differential inverse kinematics control,” in 2021 18th Conference on Robots and Vision (CRV) , 2021, pp. 64–71
2021
Cited alongside, same era.
C. Campos, R. Elvira, J. J. G. Rodríguez, J. M. Montiel, and J. D. Tardós, “Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1874–1890, 2021
2021
Cited alongside, same era.
D. Watkins-Valls, P. K. Allen, H. Maia, M. Seshadri, J. Sanabria, N. Waytowich, and J. Varley, “Mobile manipulation leveraging multiple views,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 4585–4592
2022
Cited alongside, same era.
F. Sun, Y. Chen, Y. Wu, L. Li, and X. Ren, “Motion planning and cooperative manipulation for mobile robots with dual arms,” IEEE Transactions on Emerging Topics in Computational Intelligence , vol. 6, no. 6, pp. 1345–1356, 2022
2022
Cited alongside, same era.
S. Jauhri, J. Peters, and G. Chalvatzaki, “Robot learning of mobile manipulation with reachability behavior priors,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 8399–8406, 2022
2022
Cited alongside, same era.
C. Sun, J. Orbik, C. M. Devin, B. H. Yang, A. Gupta, G. Berseth, and S. Levine, “Fully autonomous real-world reinforcement learning with applications to mobile manipulation,” in Conference on Robot Learning . PMLR, 2022, pp. 308–319
2022
Cited alongside, same era.
J. Chen, Y. Yin, T. Birdal, B. Chen, L. J. Guibas, and H. Wang, “Projective manifold gradient layer for deep rotation regression,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 6646–6655
2022
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2022
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H. Chen, X. Zang, Y. Liu, X. Zhang, and J. Zhao, “A hierarchical motion planning method for mobile manipulator,” Sensors , vol. 23, no. 15, p. 6952, 2023
2023
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2023
Closest in time.
J. Zhang, L. Dai, F. Meng, Q. Fan, X. Chen, K. Xu, and H. Wang, “3d-aware object goal navigation via simultaneous exploration and identification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 6672–6682
2023
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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 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 1757–1763
2023
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M. Bajracharya, J. Borders, R. Cheng, D. M. Helmick, L. Kaul, D. Kruse, J. Leichty, J. Ma, C. Matl, F. Michel, C. Papazov, J. Petersen, K. Shankar, and M. Tjersland, “Demonstrating mobile manipulation in the wild: A metrics-driven approach,” Robotics: Science and Systems XIX , 2023
2023
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A. B. Chowdhury, J. Li, and D. J. Cappelleri, “Neural network-based pose estimation approaches for mobile manipulation,” Journal of Mechanisms and Robotics , vol. 15, no. 1, p. 011009, 2023
2023
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