CLOSER: Evidence-Aware Active Visual Verification for Embodied Object-Goal Navigation

Pengying Wu; Mingye Jiang; Yunze Hu; Zhejia Wen; Kangjie Zhou; Zongru Yang; Zhe Peng; Chang Liu

cs.CVOct 20, 2026

CLOSER: Evidence-Aware Active Visual Verification for Embodied Object-Goal Navigation

Authors: Pengying Wu, Mingye Jiang, Yunze Hu, Zhejia Wen, Kangjie Zhou, Zongru Yang, Zhe Peng, Chang Liu

Organizations: School of Advanced Manufacturing and Robotics, Peking University, Beijing 100871, China

Abstract

Object-goal navigation (ObjectNav) requires an embodied agent to find an instance of a specified category in an unseen environment and stop near it. The agent typically relies on an object detector but must judge online from a limited egocentric video stream, sometimes a single frame, whether a detected candidate is the target. Detectors often confidently mistake similar objects, such as a sofa for a bed, and stopping at a false positive fails the episode. Prevailing systems also approach candidates greedily, leaving them occluded or partially observed. We present CLOSER, an evidence-aware active verification framework that treats stopping as closed-loop evidence acquisition, coupling vision-language model (VLM) verification with active perception. CLOSER fine-tunes an open-source VLM to label each view of a candidate tracked across frames as target, non-target, or uncertain, selects complementary views with an embedding-based diversity gate, and fuses their per-view probabilities with a lightweight network. If the fused judgment remains uncertain, a diffusion policy drives the agent toward the candidate using only the tracked mask and depth until the decision becomes decisive. CLOSER runs locally and plugs into existing ObjectNav systems without retraining them. On Gibson and Habitat-Matterport 3D, it raises average success rate by 3.6 points and success weighted by path length by 2.0 points, showing that verifying evidence before committing improves embodied navigation.

Figures & tables

Fig. 1: With CLOSER, the agent approaches an uncertain candidate and decides from closer views. Given the goal bed , the view at (1) shows a distant, partially occluded candidate and does not suffice for a decision. CLOSER therefore labels the candidate uncertain , and the agent approaches it to gather additional views (orange, dotted). At (2), the closer view reveals a sofa confuser; CLOSER labels it no , and the agent resumes exploration (blue, solid). At (3), the view of the next candidate suffices; CLOSER labels it yes , and the agent moves to the bed and stops near it. The bottom panels show the agent’s view and CLOSER’s label at each numbered location.

Fig. 2: Closed-loop inference and training of CLOSER. (A) Grounded-SAM, ByteTrack, and SAM track the candidate and refresh its mask in each RGB-D frame. (B) The evidence cache retains views selected by a CLIP-based diversity gate. (C) Per-view tri-state probabilities from the single-view perceiver are fused by a lightweight MLP. (D) After an uncertain decision, a Diffusion Transformer generates waypoints from the mask and depth. Frames observed along the waypoints re-enter panels A and B (uncertain loop). The lower panels depict the perceiver training pipeline ( Verifier Training ) and the construction of expert waypoint chunks ( Policy Training ).

	Sufficient Evidence	Insufficient Evidence
Target candidate	yes	uncertain
Non-target candidate	no	uncertain

TABLE I: Evidence-aware label assignment.

Setting	Value
Target-overlap thresholds $(\eta_{+},\eta_{-})$	$(0.25,\;0.05)$
Minimum mask-to-image ratio $\alpha$	$0.02$
Minimum box-fill ratio $\beta$	$0.40$
Maximum mean mask depth $\gamma$	$3.0$ m
Cache capacity $K$	$4$ views
View-diversity threshold $\delta_{\mathrm{view}}$	$0.18$

TABLE II: Implementation settings for CLOSER.

Plug-in comparison
Method	OV	Gibson		HM3D v1		HM3D v2
Method	OV	SR(%) $\uparrow$	SPL(%) $\uparrow$	SR(%) $\uparrow$	SPL(%) $\uparrow$	SR(%) $\uparrow$	SPL(%) $\uparrow$
ZSON [ 54 ]	✓	31.3	12.0	25.5	12.6	28.7	13.1
+ CLOSER	✓	35.6 (+4.3)	14.5 (+2.5)	29.8 (+4.3)	15.1 (+2.5)	32.5 (+3.8)	14.7 (+1.6)
DD-PPO [ 55 ]	$\times$	34.8	14.0	27.9	14.2	33.5	15.6
+ CLOSER	$\times$	38.9 (+4.1)	16.3 (+2.3)	32.1 (+4.2)	15.6 (+1.4)	36.9 (+3.4)	17.8 (+2.2)

TABLE III: End-to-end ObjectNav results. SR/SPL of eight ObjectNav systems and their CLOSER plug-in variants on Gibson, HM3D v1, and HM3D v2 (OV: open-vocabulary). Each shaded “+ CLOSER” row reports the absolute gain over its baseline in parentheses; CLOSER results are means over three seeds (standard deviations

\leq

0.9 SR and

\leq

0.6 SPL points). CLOSER improves both metrics for every system on every dataset.

Fig. 3: Paired ObjectNav gains from CLOSER. (a) Absolute SR gains grouped by dataset and by the native candidate-verification mechanism (shading, left to right: none, map-level heuristic, and candidate-level fusion). (b) Changes in SR and SPL for the same 24 system–dataset pairs; each arrow starts at the baseline and ends at its “+ CLOSER” result. Data from Table III .

Fig. 4: Closed-loop verification in one successful episode with target category sofa . (a) Evidence timeline of the two candidate events (S: step index). In event A, the initial view is deferred as uncertain , and the view retained at step 37 reveals the candidate to be a bed, so the agent discards the candidate and resumes exploration. In event B, the fused decision remains uncertain at step 72 and becomes yes at step 75, so the agent stops. The quoted text shows the perceiver rationales for the two views of event A, decoded here for illustration. (b) Top-down path and semantic map of the episode; the colored path segments correspond to the timeline arrows in (a).

Fig. 5: Real-world verification on a quadruped robot. The verification stack runs on the onboard camera stream of a Unitree Go2 in two indoor scenes (one scene per row, frames ordered left to right; insets show the onboard view). Stacked blocks occlude the candidate (a chair, top; an armchair, bottom), so the initial views are deferred as uncertain . As the robot moves around the blocks, clearer views enter the cache, and the fused decision becomes decisive once an unobstructed view is retained (rightmost insets).

Variant	SR $\uparrow$	SPL $\uparrow$	FS $\downarrow$	FR $\downarrow$	FB $\downarrow$
VLFM native stop	63.6	32.5	19.4	13.8	–
Binary GPT-4o-mini (API)	64.9	33.3	11.2	12.1	–
Binary local	64.4	33.0	13.6	14.2	–
Tri-state + diverse cache (static)	65.3	33.6	8.7	15.1	4.2
+ Diffusion policy (CLOSER)	66.1	34.1	6.4	9.3	9.8

TABLE IV: Closed-loop ablation of the verification layer. All variants are built on VLFM and evaluated on the HM3D v2 validation episodes. FS / FR / FB: episode-level false stop, false reject, and fallback trigger (%).

Fig. 6: Episode outcomes of the closed-loop ablation. Each episode is assigned to exactly one outcome; the right column shows the total stop-attributable error (FS

+

FR). Data from Table IV .

Verifier / variant	Clear $\rightarrow$ yes $\uparrow$	Low-Q tgt $\rightarrow$ unc. $\uparrow$	Clear $\rightarrow$ no $\uparrow$	Low-Q cnf $\rightarrow$ unc. $\uparrow$
Qwen2.5-VL zero-shot	68.7	42.3	55.1	39.8
Binary SFT ( yes / no )	87.4	0.0	78.6	0.0
Binary SFT + threshold	79.8	82.1	69.4	80.6
Direct-label SFT, single view	83.6	67.4	73.2	65.8
+ Rendered regimes + rationale	85.9 (+2.3)	78.7 (+11.3)	77.1 (+3.9)	77.3 (+11.5)
Full CLOSER stack	89.1 (+3.2)	85.4 (+6.7)	84.6 (+7.5)	83.8 (+6.5)

TABLE V: Verifier comparison on 5,184 held-out Grounded-SAM detections. Clear columns: correct semantic decisions on clear observations; Low-Q columns: correct deferral of insufficient observations (zero for the pure binary verifier, which cannot defer). The lower block reports the CLOSER training stages, the first two on single views; parenthesized values give the gain over the previous stage. All values in %.

Evaluation domain	Binary SFT + threshold			CLOSER (tri-state)
Evaluation domain	Defer $\uparrow$	FS $\downarrow$	FR $\downarrow$	Defer $\uparrow$	FS $\downarrow$	FR $\downarrow$
HM3D held-out (in-domain)	81.4	12.6	11.2	84.6	7.5	8.4
MP3D candidates (cross-dataset)	63.7	17.9	15.8	82.1	8.6	9.3
Rendered hard regimes	46.8	23.4	20.7	79.2	9.8	10.9

TABLE VI: Deferral quality under distribution shift. The deferral threshold of the binary verifier is selected once on the HM3D validation split and kept fixed. Defer: fraction of insufficient-evidence candidates correctly deferred; FS / FR: candidate-level false stop and false reject. All values in %.

Fig. 7: Deferral under distribution shift. The fixed binary threshold and CLOSER are evaluated on held-out HM3D candidates, MP3D candidates, and the rendered hard regimes of Table I . Annotations show changes from the in-domain to the rendered setting; the shaded region shows the gap between the methods. Data from Table VI .

Evidence input	Acc. $\uparrow$	Low-Q $\uparrow$	FS $\downarrow$
Latest frame only	80.7	78.9	11.4
Latest- $K$ frames	83.5	81.2	9.2
CLIP-diverse (ours)	86.1	84.6	7.5

TABLE VII: Ablation of cache construction. The learned components are fixed; multi-view rows use

K{=}4

. Acc.: tri-state accuracy; Low-Q: fraction of insufficient-evidence candidates correctly deferred; FS: candidate-level false stop. All values in %.

Policy	Correct $\uparrow$	Confuser $\uparrow$	Path (m) $\downarrow$	FB $\downarrow$
In-place look-around	32.4	26.1	0.00	0.0
Straight-line policy	61.7	54.3	2.84	37.6
Viewpoint re-verif. (STRIVE) [ 11 ]	70.2	63.8	2.21	25.4
Voronoi–Bézier det. BC	76.5	70.9	2.08	18.1
Voronoi–Bézier diffusion	83.6	78.4	1.69	12.7

TABLE VIII: Approach-policy comparison on initially uncertain candidates. Correct: correct yes / no decision before the close-range fallback; Confuser: rejection rate on initially uncertain confusers; FB: fallback trigger rate on this candidate subset. All values except Path are in %.

Fig. 8: Resolution accuracy, extra path, and fallback rate of approach policies on the initially uncertain candidate subset. Data from Table VIII .

Verifier	Local	Views	Tri-state accuracy $\uparrow$	Latency (s) $\downarrow$	Memory (GB) $\downarrow$	Cost/1k queries $\downarrow$
GPT-4o-mini API (tri-state)	No	1	82.4	1.38	–	0.41
Qwen2.5-VL zero-shot	Yes	1	52.6	0.31	16.9	0
CLOSER single-view perceiver	Yes	1	80.7	0.28	17.2	0
CLOSER perceiver + raw-probability MLP	Yes	$\leq 4$	86.1	0.28/view	17.2	0

TABLE IX: Verifier runtime and deployment cost. Local VLM latency and memory are measured on a single NVIDIA A100 40 GB GPU; API cost is in USD per 1,000 queries. Latency of the perceiver–MLP stack is per retained view; each MLP fusion adds 0.1 ms.

Confusion	Detector only	Binary verifier	CLOSER
Sofa as bed	79.2	53.1	35.4
Stool as chair	70.6	45.3	28.8
Sink as toilet	64.8	42.7	27.1
Other confusions	46.3	30.2	19.6

TABLE X: Candidate-level false-stop rate (%) on characteristic confuser pairs (VLFM+CLOSER, HM3D v2). “A as B” denotes a candidate of category A detected while searching for target category B; “Other confusions” pools the unlisted non-target confusion types.

Object-Goal Navigation

Object-goal navigation studies how robots find and approach requested objects in unfamiliar environments using visual, spatial, and language cues.

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Object navigation requires an agent to locate a target in an unknown environment through visual observations. Existing methods typically rely on open-vocabulary detectors or vision-language models (VLMs) to answer where to search, but often overlook what not to trust - which semantic cues are unreliable. Open-vocabulary perception is prone to systematic misleading evidence: false positives, outdated static priors, and repeated failed exploration due to lack of embodied verification, which contaminates mapping and decision-making. Such errors are rooted in structured object relations in real-world scenes. To address this, we propose DB-Nav, a framework that reshapes the search space via dual relational biases. It factorizes target-centric relations into an Activation Bias (propagates contextual evidence) and an Inhibition Bias (suppresses unreliable regions via perceptual confusion and action-level falsification). These biases are unified into a Relational Activation-Inhibition Exploration Graph that modulates frontier exploration values using online observations and failed accesses. Experiments on ObjectNav benchmarks show that DB-Nav significantly outperforms existing methods in success rate (SR) and Success weighted by Path Length (SPL), offering a lightweight, interpretable, and robust navigation framework without costly online VLM reasoning.

Abstract

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