지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.6
- 수록면
- 596 - 606 (11page)
- DOI
- 10.5302/J.ICROS.2024.23.0217
이용수
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초록· 키워드
오류제보하기
Multi-object visual navigation (MultiON) is a special type of visual navigation task that requires an embodied agent to visit multiple goal objects distributed over an unseen three-dimensional (3D) environment in a predefined order. To successfully execute MultiON, an agent should be able to accurately ground individual goal objects based on language descriptions regarding their color and shape attributes and build a semantically rich map that effectively covers the entire environment. In this paper, we propose a novel deep neural network-based agent model for performing MultiON tasks. The proposed model provides unique solutions to three different issues regarding MultiON agent design. First, the model adopts the pre-trained Grounding DINO module to ground the language descriptions of goal objects to the visual objects in input images in a zero-shot manner. Moreover, the model uses Bayesian posterior probabilities to effectively register the uncertain local contexts extracted from input images onto the global map. Finally, the model applies a novel reward function to efficiently motivate the agent to explore unvisited areas in the given environment for rapid and accurate map expansion. We demonstrate the superiority of the proposed model by conducting various quantitative and qualitative experiments using the 3D simulation platform, AI-Habitat, and the benchmark scene dataset, Matterport3D.
목차
Abstract
I. 서론
II. 관련 연구
III. 다중 물체 시각적 탐색 이동 에이전트 모델 설계
IV. 구현 및 실험
V. 결론
REFERENCES
참고문헌 (17)
참고문헌 신청
S. Wani, S. Patel, U. Jain, A. Chang, and M. Savva, “MultiON: Benchmarking semantic map memory using multi-object navigation,” Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 9700-9712, 2020. https://openreview.net/forum?id=O_wbwQg6xYO
P. Marza, L. Matignon, O. Simonin, and C. Wolf, “Teaching agents how to map: Spatial reasoning for multi-object navigation,” Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), Kyoto, pp.1725-1732, 2022. doi: https://doi.org/10.1109/IROS47612.2022.9982216
J. Kim, E. S. Lee, M. Lee, D. Zhang, and Y. M. Kim, “Sgolam: Simultaneous goal localization and mapping for multi-object goal navigation,” arXiv preprint arXiv:2110.07171, 2021. doi: https://doi.org/10.48550/arXiv.2110.07171
S. Raychaudhuri, T. Campari, U. Jain, M. Savva, and A. X. Chang, “Reduce, reuse, recycle: Modular multi-object navigation,” arXiv preprint arXiv:2304.03696, 2023. doi: https://doi.org/10.48550/arXiv.2304.03696
P. Chen, D. Ji, K. Lin, W. Hu, W. Huang, T. Li, M. Tan, and C. Gan, “Learning active camera for multi-object navigation,” Advances in Neural Information Processing Systems(NeurIPS), vol.35, pp. 28670-28682, 2022. doi: https://doi.org/10.48550/arXiv.2210.07505
P. Marza, L. Matignon, O. Simonin, and C. Wolf, “Teaching agents how to map: Spatial reasoning for multi-object navigation,” Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), Kyoto, pp.1725-1732, 2022. doi: https://doi.org/10.1109/IROS47612.2022.9982216
J. Kim, E. S. Lee, M. Lee, D. Zhang, and Y. M. Kim, “Sgolam: Simultaneous goal localization and mapping for multi-object goal navigation,” arXiv preprint arXiv:2110.07171, 2021. doi: https://doi.org/10.48550/arXiv.2110.07171
S. Raychaudhuri, T. Campari, U. Jain, M. Savva, and A. X. Chang, “Reduce, reuse, recycle: Modular multi-object navigation,” arXiv preprint arXiv:2304.03696, 2023. doi: https://doi.org/10.48550/arXiv.2304.03696
P. Chen, D. Ji, K. Lin, W. Hu, W. Huang, T. Li, M. Tan, and C. Gan, “Learning active camera for multi-object navigation,” Advances in Neural Information Processing Systems(NeurIPS), vol.35, pp. 28670-28682, 2022. doi: https://doi.org/10.48550/arXiv.2210.07505
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