Graph-MambaNav: Spatial-Temporal Graph Mamba Leveraging Object-Relation Knowledge for Object-Goal Navigation

Leyuan Sun; Genxin Chen; Linwei Ye; Yan Zhang; Xi Kan; Yanfei Sun

cs.ROAug 13, 2026

Graph-MambaNav: Spatial-Temporal Graph Mamba Leveraging Object-Relation Knowledge for Object-Goal Navigation

Authors: Leyuan Sun, Genxin Chen, Linwei Ye, Yan Zhang, Xi Kan, Yanfei Sun

Organizations: School of Internet of Things Engineering, Wuxi University, Wuxi, Jiangsu 214105, China · School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China · Wuxi Key Laboratory of Artificial Intelligence and Security, Wuxi, Jiangsu 214105, China · Wuxi University, Wuxi, Jiangsu 214105, China

Abstract

Object-goal navigation requires an agent to reason over object relationships and prioritize target-relevant objects for efficient decision making in unseen environments. While existing graph-based methods incorporate target-awareness at the feature or attention level, they remain permutation-invariant and lack an explicit mechanism to control information propagation order, limiting their ability to model target-dependent importance and long-range dependencies. In contrast, Graph-Mamba highlights that node prioritization through sequence ordering is critical for effective global reasoning. In this work, we investigate the node prioritization mechanism in Graph-Mamba and study its role in object navigation. We propose Graph-MambaNav, a target-aware spatial-temporal graph encoding framework that introduces a heuristic ordering over objects based on their relevance to the target, allowing more informative objects to be processed later to aggregate richer context. Both node ordering and edge weights are initialized from LLM-derived commonsense object relationships, providing a unified prior for structured reasoning. A spatial module integrates local message passing with global GraphMamba-based selective scanning, while a temporal module applies Mamba-based sequence modeling over object-wise temporal orders, allowing selective aggregation of historical context for long-range temporal reasoning. Experiments on AI2-THOR and RoboTHOR demonstrate improved navigation performance with generalization, and additional real-world robot deployment further validates the effectiveness of our proposed approach.

Figures & tables

Fig. 1 : Motivation and overview of Graph-MambaNav. Given an egocentric RGB observation, an object-centric graph is constructed from detected objects. LLM-derived commonsense relations provide a unified prior for both edge attribution and target-aware heuristic ordering . The ordered spatial scan enables more target-relevant objects to aggregate richer context under Mamba-based scanning, while the temporal scan models object-wise dynamics across time for action prediction.

Fig. 2 : Overview of Graph-MambaNav (Left) and its object-graph spatial-temporal encoding pipeline (Right). Given an egocentric RGB observation $\mathbf{o}_{t}$ , ResNet extracts global scene-level features $\mathbf{V}$ , while DETR provides complementary object-level features, boxes, and detection scores for object graph construction. LLM-derived commonsense priors initialize edge embeddings and define target-aware node ordering. The enlarged module on the right shows the three key graph encoding operations: local edge-aware message passing, global target-aware Graph-Mamba scanning based on object-node prioritization, and temporal scanning over object-wise memory. The target-conditioned attention fusion module produces graph feature $\mathbf{G}$ , which is fused with $\mathbf{V}$ and previous-action embedding $\mathbf{A}_{\mathrm{act}}$ for LSTM-based A3C policy learning.

Fig. 3 : Left: LLM-derived object affinity matrices across two datasets. Middle: Qualitative comparison of TSOG [ 20 ] and Graph-MambaNav in AI2-THOR, covering four different room types. Right: Graph-MambaNav results in RoboTHOR, where apartments are constructed modularly from a shared asset library. Object-relation heatmaps for the target are shown to the left of the top-down maps. In the top-down views, targets are highlighted in red and related objects in green, with the corresponding first-person observations shown below.

Method	Params	GFLOPs	GPU Memory	FPS
	(M)		(GB) per agent
T35-Transformer variant	62.13	82.39	6.72	11.32
T35-Graph-MambaNav	12.58	23.47	2.53	22.12

TABLE I : Comparison of computational complexity. We report the floating point operations (FLOPs), number of model parameters, FPS, and GPU memory consumption of the proposed T35-Graph-MambaNav and the alternative Transformer-based variant, both using a memory sequence length of 35.

Method	AI2-THOR				RoboTHOR
Method	ALL		$L\geq 5$		ALL		$L\geq 5$
	SR $\uparrow$ (%)	SPL $\uparrow$ (%)	SR $\uparrow$ (%)	SPL $\uparrow$ (%)	SR $\uparrow$ (%)	SPL $\uparrow$ (%)	SR $\uparrow$ (%)	SPL $\uparrow$ (%)
Random	3.56	1.73	0.27	0.07	-	-	-	-
Baseline [ 27 ]	57.35	33.78	45.77	30.65	26.41	16.61	17.42	12.23
SP [ 17 ]	62.16	37.01	50.86	34.17	28.04	17.63	21.66	15.14
SAVN [ 28 ]	63.32	37.62	52.38	35.31	28.42	17.82	22.13	15.34

TABLE II : Comparisons with other methods in the AI2-THOR and RoboTHOR environments

Fig. 4 : Navigation performance across varying $T$ . Red pentagon markers denote the optimal configurations.

Fig. 5 : SR versus relation variance of each target object between Graph-MambaNav and TSOG. Graph-MambaNav shows a steeper trend and higher SR, demonstrating its advantage in leveraging prior LLM-derived relational knowledge.

Method	$L\geq 10$ / $L\geq 15$ / $L\geq 20$
Method	SR $\uparrow$ (%)	SPL $\uparrow$ (%)
VTNet	42.5 / 29.6 / 10.9	29.1 / 18.5 / 6.3
DOA	54.88 / 25.18 / 10.91	37.15 / 17.83 / 8.35
TSOG	58.49 / 35.31 / 23.64	37.18 / 22.11 / 16.31
M-MambaNav	60.23 / 42.35/ 29.35	38.81 / 25.93 / 16.21
Ours w/o TS	61.23 / 41.02 / 27.32	40.33 / 28.19 / 15.65

TABLE III : Comparison under different path lengths (10, 15, and 20) in AI2-THOR, where TS denotes Temporal Scan

Setting	SR $\uparrow$ (%)	SPL $\uparrow$ (%)
Top-5 ordered nodes	$77.05{\pm}0.91$	$43.10{\pm}0.76$
Top-10 ordered nodes	$81.56{\pm}0.78$	$45.65{\pm}0.64$
Top-15 ordered nodes	$82.63{\pm}0.72$	$46.32{\pm}0.59$
Top-20 ordered nodes	$82.84{\pm}0.69$	$46.30{\pm}0.57$
All-22 ordered nodes	$\textbf{83.22}{\pm}0.75$	$\textbf{46.52}{\pm}0.61$
All-22 + 10% noisy relations	$82.31{\pm}0.83$	$45.51{\pm}0.71$

TABLE IV : Analysis of ordering scalability and robustness to noisy object-relation priors. Due to the fixed object categories in the offline benchmark, we vary the number of nodes using target-aware ordering and perturb the LLM-derived affinity matrix to simulate noisy relations.

ID		Method			ALL / $L\geq 5$
ID	LS	GS	TS	SR $\uparrow$ (%)	SPL $\uparrow$ (%)
I	1				63.83 / 52.45	33.75 / 30.33
	2	$\checkmark$			66.15 / 61.42	41.35 / 35.43
	3	$\checkmark$	$\checkmark$		78.34 / 71.66	43.39 / 42.85
	4	$\checkmark$	$\checkmark$	$\checkmark$	83.22 / 76.09	46.52 / 46.20
II	5	Ours w/ F-R LS edges			66.26 / 62.98	40.34 / 38.23

TABLE V : Ablation study, impacts of different components. All variants in Group II retain the full LS+GS+TS pipeline and modify only the specified relation prior or scan order.

Fig. 6 : Real-world navigation illustration. Left: robot platform; middle: trajectory visualization; right: perception results. Bottom: object relation heatmap for the target book . The point cloud and odometry are for visualization only and are not accessible to the robot.

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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SAGE-Nav: Leveraging LLM Planning and Alignment Fusion for Hierarchical Scene Graph-Guided Navigation

Hao Su, Yuehao Huang, Yukai Ma, Yong Liu+1

Zhejiang University, Hangzhou, China

Robot Policy Learning Graph Neural Networks Large Language Model-Based Robot Planning Robot Navigation Object-Goal Navigation

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Object-Goal Navigation (ObjNav) requires embodied agents to autonomously locate specified targets using only egocentric visual observations. Existing monolithic methods struggle with long-horizon reasoning and generalize poorly to novel environments. To address these limitations, we propose SAGE-Nav, a novel hierarchical framework that integrates the reasoning capabilities of Large Language Models (LLMs) with dynamic scene graphs. Crucially, it decouples asynchronous global semantic planning from the high-frequency reactive control loop. The LLM serves as a global planner, decomposing abstract instructions into a sequence of semantically grounded waypoints. To translate these plans into dense multi-modal guidance, we design a Hierarchical Scene Graph Encoder (HSGE) that leverages relational graph convolutions to produce structure-aware embeddings preserving both semantic and spatial topology. Furthermore, we develop the Goal-aware Alignment-Fusion Network (GAFN) to dynamically fuse real-time perception with these structural priors. Using an adaptive gating mechanism with an explicit inductive bias, GAFN ensures robust visual-topological alignment for the low-level policy. Extensive evaluations in the i-THOR and RoboTHOR environments demonstrate that SAGE-Nav achieves state-of-the-art performance, delivering substantial gains in navigation efficiency and zero-shot generalization while maintaining the low control latency required for physical robotic deployment.

Abstract

Object-Goal Navigation

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