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Significant progress has been made in open-vocabulary mobile manipulation, where the goal is for a robot to perform tasks in any environment given a natural language description.
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2018
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2020
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2020
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M. Henein, J. Zhang, R. Mahony, and V. Ila, “Dynamic slam: The need for speed,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 2123–2129
2020
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2021
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M. Hahn, D. S. Chaplot, S. Tulsiani, M. Mukadam, J. M. Rehg, and A. Gupta, “No rl, no simulation: Learning to navigate without navigating,” Advances in Neural Information Processing Systems , vol. 34, pp. 26 661–26 673, 2021
2021
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N. Yokoyama, S. Ha, and D. Batra, “Success weighted by completion time: A dynamics-aware evaluation criteria for embodied navigation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 1562–1569
2021
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X. Zhao, H. Agrawal, D. Batra, and A. G. Schwing, “The surprising effectiveness of visual odometry techniques for embodied pointgoal navigation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 16 127–16 136
2021
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2021
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2021
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2022
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2022
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2022
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2022
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E. Michael, T. Summers, T. A. Wood, C. Manzie, and I. Shames, “Probabilistic data association for semantic slam at scale,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 4359–4364
2022
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2022
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2022
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2023
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D. Maggio, M. Abate, J. Shi, C. Mario, and L. Carlone, “Loc-nerf: Monte carlo localization using neural radiance fields,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 4018–4025
2023
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2023
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2023
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D. F. Henning, T. Laidlow, and S. Leutenegger, “Bodyslam: Joint camera localisation, mapping, and human motion tracking,” in European Conference on Computer Vision . Springer, 2022, pp. 656–673
2022
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2022
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2022
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2022
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J. Wong, A. Tung, A. Kurenkov, A. Mandlekar, L. Fei-Fei, S. Savarese, and R. Martín-Martín, “Error-aware imitation learning from teleoperation data for mobile manipulation,” in Conference on Robot Learning . PMLR, 2022, pp. 1367–1378
2022
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2023
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2023
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R. Ramrakhya, D. Batra, E. Wijmans, and A. Das, “Pirlnav: Pretraining with imitation and rl finetuning for objectnav,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2023, pp. 17 896–17 906
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2023
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2023
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H.-S. Fang, C. Wang, H. Fang, M. Gou, J. Liu, H. Yan, W. Liu, Y. Xie, and C. Lu, “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,” IEEE Transactions on Robotics , 2023
2023
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2024
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2024
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H. Matsuki, R. Murai, P. H. Kelly, and A. J. Davison, “Gaussian splatting slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 18 039–18 048
2024
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C. Yan, D. Qu, D. Xu, B. Zhao, Z. Wang, D. Wang, and X. Li, “Gs-slam: Dense visual slam with 3d gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 595–19 604
2024
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L. Schmid, M. Abate, Y. Chang, and L. Carlone, “Khronos: A unified approach for spatio-temporal metric-semantic slam in dynamic environments,” in Proc. of Robotics: Science and Systems , 2024
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O. Team, “Gpt-4 technical report,” 2024. [Online]. Available: https://arxiv.org/abs/2303.08774
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A. Majumdar, A. Ajay, X. Zhang, P. Putta, S. Yenamandra, M. Henaff, S. Silwal, P. Mcvay, O. Maksymets, S. Arnaud, et al. , “Openeqa: Embodied question answering in the era of foundation models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 16 488–16 498
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N. Yokoyama, S. Ha, D. Batra, J. Wang, and B. Bucher, “Vlfm: Vision-language frontier maps for zero-shot semantic navigation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 42–48
2024
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