G2WAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation
Authors: Ke Gui, Chenghao Shi, Ziyue Wang, Longwen Chen, Xieyuanli Chen, Huimin Lu
Organizations: College of Intelligence Science and Technology, National University of Defense Technology, China · National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, China · School of Future Technology, National University of Defense Technology, China
World models capture environment dynamics and support counterfactual reasoning for planning. In image-goal navigation, where the target often lies beyond the current field of view, the agent must not only model environmental dynamics but also maintain a persistent spatial memory of the scene. Existing approaches either introduce spatial context by fine-tuning pretrained models on the target task, which underexploits their spatial priors, or rely on external 3D reconstruction, which is computationally expensive for real-world deployment. In this paper, we propose the Geometry Grounded World Action Model (G2WAM) for image-goal navigation. G2WAM encodes spatial cues through a learned scene-coordinate localization module that anchors the current, goal, and history frames in a common reference frame, yielding a stable and consistent spatial context that enables efficient navigation without explicit 3D reconstruction. Following the world-action-model paradigm, G2WAM jointly predicts future states and actions, eliminating the need for costly cross-entropy method (CEM) style planning. In addition, G2WAM operates entirely in latent space, avoiding the computational burden of pixel-level reconstruction and substantially reducing inference cost. Across four trajectory prediction benchmarks, G2WAM reduces average ATE and RPE by 40.1% and 35.2%, respectively. In closed-loop navigation experiments, it improves the success rate from 54.0% to 63.0% and the success weighted by path length (SPL) from 36.4% to 59.2%, while maintaining high efficiency.
Figures & tables
Figure 1: Navigation world model paradigms. Conventional world models plan over predicted observations, whereas world action models jointly predict observations and actions. G 2 WAM further leverages estimated spatial context to efficiently roll out future latents and executable actions.
Figure 2: Overview of G 2 WAM. A learned localizer anchors the goal and observed frames in a shared scene frame to obtain the goal-relative pose and causal history motions. The goal image and pose guide joint latent and waypoint prediction, while the history motions condition the action branch.
Method
RECON
SACSoN
TartanDrive
SCAND
Average
GNM ( Shah et al., 2023a )
1.87/0.73
3.71 /1.00
6.65/1.62
2.12/1.61
3.59/1.24
NoMaD ( Sridhar et al., 2024 )
1.95/0.53
3.73/0.96
6.32/1.31
2.24/0.49
3.56/0.82
NWM ( Bar et al., 2025 )
1.13 / 0.35
4.12/0.96
5.63/1.18
1.28/ 0.33
3.04 / 0.71
DINO-WM ( Zhou et al., 2025 )
2.33/0.54
5.39/1.10
5.00/ 1.06
1.87/0.42
3.65/0.78
DreamerV3 ( Hafner et al., 2025 )
1.88/ 0.34
4.86/1.01
5.96/1.24
1.38/0.34
3.52/0.73
LeWM ( Maes et al., 2026 )
2.20/0.66
4.01/1.05
4.93 /1.31
2.16/0.58
3.33/0.90
Table 1: 8-step trajectory prediction. Bold denotes the best and underlined the second-best value among non-GT methods.
Figure 3: 8-step trajectory rollouts on RECON, SACSoN, TartanDrive, and SCAND, with all paths aligned at the rollout origin.
Table 5
Dataset
Pose + motion
Pose only
None
GT+GT
Pred.+GT
Pred.+Pred.
GT
Pred.
RECON
0.46/0.18
1.17/0.33
1.38/0.36
0.77/0.33
1.38/0.37
2.02/0.58
SACSoN
0.94/0.35
1.15/0.39
1.18/0.40
1.42/0.48
1.42/0.46
2.32/0.62
TartanDrive
1.13/0.36
1.70/0.48
2.70/0.63
3.56/0.96
4.53/0.93
5.64/1.19
SCAND
0.57/0.23
1.55/0.38
2.04/0.45
0.78/0.29
1.96/0.44
1.96/0.47
Average
0.78/0.28
1.39/0.39
1.82/0.46
1.63/0.52
2.32/0.55
2.99/0.71
Table 4: Conditioning ablation under the 8-step trajectory-prediction protocol.
Table 7
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Episode index
Episode ID
G 2 WAM
Steps
NWM
Steps
92
3353
S
9
S
75
17
3403
S
21
F
100
29
3532
S
60
S
36
19
3537
S
32
F
100
59
3310
S
12
S
1
16
3527
S
25
S
4
Appendix
Table 6: Outcome-selected paired Habitat recordings. S/F denotes success or failure under the 2 m threshold. Steps are executed actions, not video frames. These examples do not estimate full-test-set SR or SPL.
Goal-conditioned visual navigation requires a robot to act under partial observability by anticipating how its motion will change the future egocentric view and whether that change brings it closer to the goal. Navigation world models provide such visual foresight, but they remain prediction modules that require an external planner to convert predicted futures into closed-loop control. We propose Navigation World Action Model (NavWAM), a diffusion-transformer policy that turns navigation world-model prediction into executable action by representing future observations, goal-progress values, and action chunks in a shared latent sequence. By learning future prediction jointly with the action and value targets that determine closed-loop behavior, NavWAM makes visual foresight directly usable for robot control. We build NavWAM through simulation pretraining and real-robot adaptation, and evaluate it on image-goal navigation against planning-based world models and a representative direct navigation policy. Across offline benchmarks and closed-loop real-robot deployment, NavWAM improves over planning-based world-model baselines in our evaluations while using the default policy mode without CEM-style action search. Project page: https://dachii-azm.github.io/navwam/
Daichi Azuma, Taiki Miyanishi, Koya Sakamoto +6
The University of Tokyo · National Institute of Informatics · AIRoA +1
Image-goal navigation with latent world models requires not only accurate future prediction, but also a planning cost that reliably ranks candidate action sequences. We define the cost as the cosine distance between the predicted future embedding and the goal embedding, and show that poor cost ordering can mislead sampling-based planners such as Cross-Entropy Method (CEM). To address this, we propose a latent world model built on a frozen DINO-family encoder and train it with two complementary objectives. An autoregressive rollout loss reduces the gap between training and multi-step planning rollouts, while a Monotone Cost Ranking (MCR) loss directly encourages increasingly perturbed action sequences to receive higher planning costs. We also study InfoNCE-based action-contrastive training and find that temporal permutation negatives distort the latent geometry and degrade planning performance. On the GNM navigation dataset, our method outperforms Navigation World Models (NWM), DINO-WM, OmniVLA, and NoMaD, achieving state-of-the-art image-goal navigation performance while reducing orientation error by 2.7× over the same-encoder DINO WM baseline. We also deploy the model zero-shot on a physical robot, where it follows goal-directed paths in unseen indoor and outdoor environments.
World models enable planning in imagined future predicted space, offering a promising framework for embodied navigation. However, existing navigation world models often lack action-conditioned consistency, so visually plausible predictions can still drift under multi-step rollout and degrade planning. Moreover, efficient deployment requires few-step diffusion inference, but existing distillation methods do not explicitly preserve rollout consistency, creating a training-inference mismatch. To address these challenges, we propose MWM, a mobile world model for planning-based image-goal navigation. Specifically, we introduce a two-stage training framework that combines structure pretraining with Action-Conditioned Consistency (ACC) post-training to improve action-conditioned rollout consistency. We further introduce Inference-Consistent State Distillation (ICSD) for few-step diffusion distillation with improved rollout consistency. Our experiments on benchmark and real-world tasks demonstrate consistent gains in visual fidelity, trajectory accuracy, planning success, and inference efficiency. Code: https://github.com/AIGeeksGroup/MWM. Website: https://aigeeksgroup.github.io/MWM.