Zero-shot object-goal navigation aims to find target objects in unseen environments using only egocentric observation. Recent methods leverage foundation models' comprehension and reasoning capabilities to enhance navigation performance. However, when faced with poor viewpoints or weak semantic cues, foundation models often fail to support reliable reasoning in both perception and planning, resulting in inefficient or failed navigation. We observe that inherent relationships among objects and regions encode structured scene priors, which help agents infer plausible target locations even under partial observations. Motivated by this insight, we propose Spatial Relation-aware Navigation (SR-Nav), a framework that models both observed and experience-based spatial relationships to enhance both perception and planning. Specifically, SR-Nav first constructs a Dynamic Spatial Relationship Graph (DSRG) that encodes the target-centered spatial relationships through the foundation models and updates dynamically with real-time observations. We then introduce a Relation-aware Matching Module. It utilizes relationship matching instead of naive detection, leveraging diverse relationships in the DSRG to verify and correct errors, enhancing visual perception robustness. Finally, we design a Dynamic Relationship Planning Module to reduce the planning search space by dynamically computing the optimal paths based on the DSRG from the current position, thereby guiding planning and reducing exploration redundancy. Experiments on HM3D show that our method achieves state-of-the-art performance in both success rate and navigation efficiency. The code will be publicly available at https://github.com/Mzyw-1314/SR-Nav
Figures & tables
Fig. 1: (a) Our Spatial Relationship Graph (SRG) integrates experiential spatial priors and perceptual observations to enhance target perception via relational matching and guide efficient path planning through spatial reasoning. (b) Comparison of successful episodes by step count: our perception+experience approach (red) achieves higher success rates at fewer exploration steps than perception-only methods (blue), demonstrating that experiential knowledge provides effective guidance in early, observation-limited navigation.
Fig. 2: Comparison with existing methods. (a) Geometry-based methods perceive targets via object detection and solely rely on geometric cues for planning. (b) Semantic-based methods incorporate target semantics to guide planning. (c) Our method employs spatial relationship matching for target perception and utilizes target-associated spatial relationships for planning guidance.
Fig. 3: Compared to scene graphs, which are limited by perception and mostly target-irrelevant, our method leverages both observed and experience-based relationships, which are predictive and target-relevant, enabling more effective navigation guidance.
Fig. 4: Overview of the SR-Nav. The LLM initializes a Dynamic Spatial Relationships Graph (DSRG) for the target. At each time-step, the agent collects RGB-D observations and updates the DSRG via VLM reasoning. The RAMM module corrects FP/FN errors using spatial priors. When the target is not detected, DRPM integrates DSRG localization and relational cues to generate prompts, ranks scene regions via VLM semantic similarity, and selects the highest-scoring frontier for local navigation.
Fig. 5: Relationship-aware Matching Module. This module refines raw detections using target-related spatial priors from the DSRG. It uses relationship matching to suppress false positives and identify potential false negatives, ensuring more reliable detection results.
Fig. 6: Examples of RAMM for perception.
Fig. 7: Examples of DRPM for planning.
Method
Venue
Unsupervised
Zero-shot
HM3D
MP3D
SR ↑
SPL ↑
SR ↑
SPL ↑
SemExp
NeurIPS’20
✗
✗
37.9
18.8
36.0
14.4
PONI
CVPR’22
✗
✗
-
-
31.8
12.1
SGM
CVPR’24
✗
✗
60.2
30.8
37.7
14.7
ZSON
NeurIPS’22
✗
✓
25.5
12.6
15.3
4.8
PixNav
ICRA’24
✗
✓
37.9
20.5
-
-
TABLE I: Zero-shot object navigation results on HM3D v0.1 and MP3D benchmarks.
Fig. 8: Qualitative comparison of navigation trajectories on the HM3D v0.1 dataset. Rows 1 and 2 demonstrate higher navigational efficiency and overall success rate of our method, respectively.
Fig. 9: Visualization of the navigation process of SR-Nav.
Fig. 10: Validation of RAMM and DRPM. (a) Superior detection precision and recall across all object categories via spatial relationship matching. (b) Higher semantic similarity scores throughout planning with spatial relationship guidance.
Base
DRPM
RAMM
SR
SPL
Object
Region
(%)
(%)
✓
✗
✗
✗
52.5
30.3
✓
✓
✗
✗
53.1
29.2
✓
✓
✓
✗
54.5
32.3
✓
✓
✓
✓
58.3
33.0
TABLE II: An ablation study of different modules on HM3D v0.1. ’Object’ and ’Region’ indicate the incorporation of object-level and region-level spatial relations during planning, respectively. ’RAMM’ denotes the Relation-Aware Matching Module, which is used to verify and correct detection results.
Method
Model
SR(%)
SPL(%)
Times ↓ (min/episode)
SG-Nav
LLaMA-7B
53.9
24.8
13
WMNav
Qwen-2.5-VL-7B
46.1
20.7
2.0
Gemini 1.5 Pro
58.1
31.2
–
ours
InternVL-3-8B
55.1
32.6
1.5
Qwen-2.5-VL-7B
58.3
33.0
TABLE III: Performance and computational efficiency comparison.
Disturbance Type
Prompt Content
SR(%) ↑
SPL(%) ↑
Seems like there is a < target >ahead
55.4
32.1
A < target >can be in the vicinity.
56.5
32.3
Template
Seems like a < target >ahead
55.7
32.4
You may find < target >nearby
55.3
30.9
< target >
51.3
28.4
W/o Rdis
56.0
31.5
TABLE IV: Prompt Sensitivity and Relationship Ablation Study.
Method
bed
chair
plant
sofa
toilet
tv_monitor
Average
L3MVN
52.9
51.6
46.4
50.1
41.5
54.2
49.5
VLFM
50.1
66.6
44.0
48.1
58.5
34.9
52.5
SR-Nav (ours)
55.4
68.9
42.8
54.1
65.3
44.8
58.3
SR-Nav (ours) ∗
-
-
-
-
-
-
61.3 ∗
TABLE V: Performance comparison on object-goal navigation across six target categories and average. Best results are in bold . We manually verified and annotated the corresponding results. Entries based on manual annotation are marked with an asterisk ( ∗ ).
Fig. 11: Real-world deployment. SR-Nav enables zero-shot object navigation in diverse and complex real-world environments by leveraging experience-based spatial priors to guide exploration and correct target detection results.