Page 345 - 《软件学报》2025年第4期
P. 345
陈铂垒 等: 面向具身人工智能的物体目标导航综述 1751
free learning. In: Proc. of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022.
18868–18878. [doi: 10.1109/CVPR52688.2022.01832]
[21] Zhu MZ, Zhao BL, Kong T. Navigating to objects in unseen environments by distance prediction. In: Proc. of the 2022 IEEE/RSJ Int’l
Conf. on Intelligent Robots and Systems. Kyoto: IEEE, 2022. 10571–10578. [doi: 10.1109/IROS47612.2022.9981766]
[22] Zhang JZ, Dai L, Meng FP, Fan QN, Chen XL, Xu K, Wang H. 3D-aware object goal navigation via simultaneous exploration and
identification. In: Proc. of the 2023 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023. 6672–6682.
[doi: 10.1109/CVPR52729.2023.00645]
[23] Min SY, Tsai YHH, Ding W, Farhadi A, Salakhutdinov R, Bisk Y, Zhang J. Object goal navigation with end-to-end self-supervision.
arXiv:2212.05923, 2022.
[24] Yang W, Wang XL, Farhadi A, Gupta A, Mottaghi R. Visual semantic navigation using scene priors. In: Proc. of the 7th Int’l Conf. on
Learning Representations. New Orleans: ICLR, 2019.
[25] Wani S, Patel S, Jain U, Chang AX, Savva M. MultiON: Benchmarking semantic map memory using multi-object navigation. In: Proc.
of the 34th Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2020. 813.
[26] Raychaudhuri S, Campari T, Jain U, Savva M, Chang AX. Reduce, reuse, recycle: Modular multi-object navigation. arXiv:2304.03696,
2023.
[27] Sadek A, Bono G, Chidlovskii B, Baskurt A, Wolf C. Multi-object navigation in real environments using hybrid policies. In: Proc. of the
2023 IEEE Int’l Conf. on Robotics and Automation (ICRA). London: IEEE, 2023. 4085–4091. [doi: 10.1109/ICRA48891.2023.
10161030]
[28] Zeng HT, Song XH, Jiang SQ. Multi-object navigation using potential target position policy function. IEEE Trans. on Image Processing,
2023, 32: 2608–2619. [doi: 10.1109/TIP.2023.3263110]
[29] Marza P, Matignon L, Simonin O, Wolf C. Multi-object navigation with dynamically learned neural implicit representations. In: Proc. of
the 2023 IEEE/CVF Int’l Conf. on Computer Vision. Paris: IEEE, 2023. 10970–10981. [doi: 10.1109/ICCV51070.2023.01010]
[30] Chen PH, Ji DY, Lin KY, Hu WW, Huang WB, Li TH, Tan MK, Gan C. Learning active camera for multi-object navigation. In: Proc. of
the 36th Int’l Conf. on Neural Information Processing Systems. New Orleans: Curran Associates Inc., 2022. 2078.
[31] Ye J, Batra D, Das A, Wijmans E. Auxiliary tasks and exploration enable objectgoal navigation. In: Proc. of the 2021 IEEE/CVF Int’l
Conf. on Computer Vision. Montreal: IEEE, 2021. 16097–16106. [doi: 10.1109/ICCV48922.2021.01581]
[32] Zhang SX, Song XH, Bai YB, Li WJ, Chu YK, Jiang SQ. Hierarchical object-to-zone graph for object navigation. In: Proc. of the 2021
IEEE/CVF Int’l Conf. on Computer Vision. Montreal: IEEE, 2021. 15110–15120. [doi: 10.1109/ICCV48922.2021.01485]
[33] Li WJ, Song XH, Bai YB, Zhang SX, Jiang SQ. ION: Instance-level object navigation. In: Proc. of the 29th ACM Int’l Conf. on
Multimedia. ACM, 2021. 4343–4352. [doi: 10.1145/3474085.3475575]
[34] Mayo B, Hazan T, Tal A. Visual navigation with spatial attention. In: Proc. of the 2021 IEEE/CVF Conf. on Computer Vision and
Pattern Recognition. Nashville: IEEE, 2021. 16893–16902. [doi: 10.1109/CVPR46437.2021.01662]
[35] Li WY, Hong RX, Shen JW, Yuan L, Lu Y. Transformer memory for interactive visual navigation in cluttered environments. IEEE
Robotics and Automation Letters, 2023, 8(3): 1731–1738. [doi: 10.1109/LRA.2023.3241803]
[36] Du HM, Yu X, Zheng L. Learning object relation graph and tentative policy for visual navigation. In: Proc. of the 16th European Conf.
on Computer Vision. Glasgow: Springer, 2020. 19–34. [doi: 10.1007/978-3-030-58571-6_2]
[37] Du HM, Li LC, Huang Z, Yu X. Object-goal visual navigation via effective exploration of relations among historical navigation states.
In: Proc. of the 2023 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023. 2563–2573. [doi: 10.1109/
CVPR52729.2023.00252]
[38] Zhang SX, Song XH, Li WJ, Bai YB, Yu XY, Jiang SQ. Layout-based causal inference for object navigation. In: Proc. of the 2023
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023. 10792–10802. [doi: 10.1109/CVPR52729.2023.
01039]
[39] Wortsman M, Ehsani K, Rastegari M, Farhadi A, Mottaghi R. Learning to learn how to learn: Self-adaptive visual navigation using meta-
learning. In: Proc. of the 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 6743–6752.
[doi: 10.1109/CVPR.2019.00691]
[40] Zhang SX, Li WJ, Song XH, Bai YB, Jiang SQ. Generative meta-adversarial network for unseen object navigation. In: Proc. of the 17th
European Conf. on Computer Vision. Tel Aviv: Springer, 2022. 301–320. [doi: 10.1007/978-3-031-19842-7_18]
[41] Zhai A, Wang SL. PEANUT: Predicting and navigating to unseen targets. In: Proc. of the 2023 IEEE/CVF Int’l Conf. on Computer
Vision. Paris: IEEE, 2023. 10892–10901. [doi: 10.1109/ICCV51070.2023.01003]
[42] Pal A, Qiu YD, Christensen H. Learning hierarchical relationships for object-goal navigation. In: Proc. of the 2020 Conf. on Robot
Learning. Cambridge: PMLR, 2021. 517–528.