Papers › SOON: Scenario Oriented Object Navigation with Graph-based Exploration

SOON: Scenario Oriented Object Navigation with Graph-based Exploration

31 Mar 2021CVPR 2021 1arXiv:2103.17138archive 2025-07-28

Fengda Zhu, Xiwen Liang, Yi Zhu, Xiaojun Chang, Xiaodan Liang

The ability to navigate like a human towards a language-guided target from anywhere in a 3D embodied environment is one of the 'holy grail' goals of intelligent robots. Most visual navigation benchmarks, however, focus on navigating toward a target from a fixed starting point, guided by an elaborate set of instructions that depicts step-by-step. This approach deviates from real-world problems in which human-only describes what the object and its surrounding look like and asks the robot to start navigation from anywhere. Accordingly, in this paper, we introduce a Scenario Oriented Object Navigation (SOON) task. In this task, an agent is required to navigate from an arbitrary position in a 3D embodied environment to localize a target following a scene description. To give a promising direction to solve this task, we propose a novel graph-based exploration (GBE) method, which models the navigation state as a graph and introduces a novel graph-based exploration approach to learn knowledge from the graph and stabilize training by learning sub-optimal trajectories. We also propose a new large-scale benchmark named From Anywhere to Object (FAO) dataset. To avoid target ambiguity, the descriptions in FAO provide rich semantic scene information includes: object attribute, object relationship, region description, and nearby region description. Our experiments reveal that the proposed GBE outperforms various state-of-the-arts on both FAO and R2R datasets. And the ablation studies on FAO validates the quality of the dataset.

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zhufengdaaa/soon officialpytorch report

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1ran · our draft was wrong
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batch_diagonal zhufengdaaa/soon/r2r_src/nav_graph.py official repository ran · fixture could not drive it MIT (permissive) · 3cef21d540016e09 · report
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sparse_mx_to_torch_sparse_tensor identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · c97b99c4e8201a97 · report

Tasks

AttributeNavigateObjectVisual Navigation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Visual Navigation SOON Test GBE Nav-SPL 13.3 #6 of 6 Archive leaderboard report
Visual Navigation SOON Test GBE SR 19.5 #6 of 6 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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