Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-Language Navigation
Authors: Meng Wei
Chenyang Wan
Jiaqi Peng
Xiqian Yu
Yuqiang Yang
Delin Feng
Wenzhe Cai
Chenming Zhu
Tai Wang
Jiangmiao Pang
Xihui Liu
Venue: ICLR 2026 Poster (2026)
Keywords: navigation foundation models
Vision-and-Language Navigation
Primary area: foundation or frontier models, including LLMs

Abstract: While recent large vision-language models (VLMs) have improved generalization in vision-language navigation (VLN), existing methods typically rely on end-to-end pipelines that map vision-language inputs directly to short-horizon discrete actions. Such designs often produce fragmented motions, incur high latency, and struggle with real-world challenges like dynamic obstacle avoidance.
We propose DualVLN, the first dual-system VLN foundation model that synergistically integrates high-level reasoning with low-level action execution. System 2, a VLM-based global planner, "grounds slowly" by predicting mid-term waypoint goals via image-grounded reasoning. System 1, a lightweight, multi-modal conditioning Diffusion Transformer policy, "moves fast" by leveraging both explicit pixel goals and latent features from System 2 to generate smooth and accurate trajectories.
The dual-system design enables robust real-time control and adaptive local decision-making in complex, dynamic environments. By decoupling training, the VLM retains its generalization, while System 1 achieves interpretable and effective local navigation. DualVLN outperforms prior methods across all VLN benchmarks and real-world experiments demonstrate robust long-horizon planning and real-time adaptability in dynamic environments.

Published as a conference paper at ICLR 2026
GROUND SLOW, MOVE FaST: A DUAL-SYSTEM FOUNDATION MODEL FOR GENERALIZABLE VISION- AND-LANGUACE NAVIGATION
Meng Wei Chenyang Wan Jiaqi Peng Xiqian Yu' Yuqiang Yang* Delin Fengt Wenzhe Cai Chenming Zhu 1.2 Tai Wang Jiangmiao Pangl: Xihui Liu
Shanghaii AI Laboratory Thhe University of Hong Kong
*Zejjang University "Tsinghua University
Project Lead Corresponding authors
Q Code: InternÑav
Model: InternVIA- N1
S
Data: InternData-N1
A Homepage
ABSTRACT
While recent large vision-language models (VLMs) have improved generalization in vision-languaag navigation (VLN), existing methods typically rely on end-to- end pipelines that map vision-language inputs directly to short-horizon discrete actions. Such designs often produce fragmented motions, incur high latency, and struggle with real-world challenges like dynamic obstacle avoidance. We propose DualVLN, the first dual-system VI.N foundation model that synergistically inte- grates high-level reasoning with low-level action execution. System 2, a VL.M- based global planner, "grounds slowly" by predicting mid-term waypoint goals via image-grounded reasoning. System 1, lightweight, multi-modal condition- ing Diffusion Transformer policy, "moves fast" by leveraging both explicit pixel goals and latent features from System 2 to generate smooth and accurate trajecto- ries. The dual dual-system design enables robust real-time control and adaptive local decision-making in complex, dynamic environments. By decoupling training, the VLM retains its generalization, while System achieves interpretable and effec- tive local navigation. DualVLN outperforms prior methods across all VLN bench- marks and real-world experiments demonstrate robust long-horizon planning and real-time adaptability in dynamic environments.
Ground Slow
VLN-CE
PSS PGSI Cisc
Eniget Daty
37.4
S Mastery DiAe
System 2
System 1 Action
78 Pretrained VLM
Diffusion Policy
Contollar
NIVILA PolNd SHemAT
Indrucsie
VLN-PE
$1.5
2 Hz
30 Hiz
Argecheanos
16.
Slow
3 Fast
Sog
CMA
NoM
Dynamic Avoidance
Continuous Trajectory
NaVILA
StreamMVL
DuaiVLN
Figure 1: The proposed dual-system framework decouples high-level reasoning from low-level con- trol. System 2 (slow, 2 Hz) uses 7B pretrained VLM to generate pixel goal and latent goal, while System (fast, 30 Hz) a lightweight diffusion-based policy that converts the goals into smooth trajectories with high- feghfrreuency RGB inputs. The asynchronous inference enables continuous and smooth navigation process. DualVLN sets a new state-of-the-arr on VLN-CE and VLN-PE, and shows strong generalization in real-world deployments.

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INTRODUCTION
Vision-Language Navigation (VLN) is a critical task in robotics. A VLN system receives language instructions with visual observations as input and plans a trajectory toward the goal. Recently, this field has witnessed substantial progress, evolving from early benchmarks that focus on discrete goal planning Anderson et al. (2018): Ku et al. (2020), to continuous action space formulations Krantz et al. (2020b), and further to physically realistic simulations with locomotion controllers Wang et al. (2025b); Cheng et al. (2025). Meanwhile, large vision language models (VLMs) offer new potential for VIN, as their strong prior knowledge can be transferred through post-training to empower VLN systems with unprecedented generalization across diverse instructions and environments.
However, even in continuous VLN benchmarks, existing Aasion-Laguuag-Acttion (VLA) mod- els Zhang et al. (2025a); Cheng et al. (2025); Zheng et al. (2024); Wei et al. (2025) largely adopt tightly coupled end-to-end paradigm, mapping vision and language inputs directly to short-horizon discrete actions (e.g., move forward 0.25 m). Such design introduces critical limitations for real- world deployment. First, it produces fragmented and unnatural motions, leading to high execution latency since every step depends on frequent calls large VLMs. Second, by entangling vision- language reasoning, global planning, and local control into a single pipeline, these models lack explicit coordination across hierarchical decision levels. Consequently, they fall short in meeting advanced requirements such as agile control and dynamic obstacle avoidance.
To overcome these limitations, we propose the first dual-system VLN foundation model DualVLN that explicitly bridges the reasoning strength of VLMs with the agility required for real-time control. DualVLN decouples the VLN pipeline into two complementary systems. System 2, large foun- dation VLM, performs slow but robust reasoning and produces explicit intermediate pixel goals. System 1, a lightweight diffusion based policy model, transforms the grounded targets into contin- uous traversable trajectories, enabling robust collision avoidance in dynamic scenarios. For better coordination between System and System 2, we connect the two systems through latent represen- tations. After the System 2 is trained with the pixel goal grounding task, freeze the weights of System 2. Then we introduce a set of learnable latent queries and optimize them via prompt tuning. These queries extract compact latent features and serve implicit goals for System 1.
Why decoupled sequential training? Decoupling enables each system to specialize: System 2 can scale with large multi-source reasoning data, while System needs only few low-level goal reach- ing data. System 1 further benefits from additional high frequent RGB inputs and asynchronous inference to achieve higher control frequency in dynamic settings. Crucially, this separation pre- serves the VLM's generalization when adapting to downstream low-level planning.
Why use both explicit pixel goal and implicit latent goal? Relying solely on explicit 2D pixel goals as guidance for System fails to fully exploit the rich hidden features of the VLM, resulting in shallow connection between reasoning and local planning and reducing the dual-system a mod- ular pipeline. Learning explicit pixel goals enhances System 2's interpretability and generalization. Building upon this, implicit latent features further provide richer and more adaptive guidance for System 1, enabling it to automatically extract task-relevan representations from the heterogeneous information encoded in the VLM's hidden states.
Experimental results show that DuaIVLN consistently surpasses prior state-of-the-art methods on both VLN-CE Krantz et al. (2020b) and VIN--PE Wang al. (2025b) benchmarks. Real-world evaluations demonstrate its robust long-horizon planning. real-time trajectory execution, and dy- namic obstacle avoidance across multiple robot platforms and diverse scenarios. We also introduce the first Social- Social-VLN VLN benchmark to evaluate navigation models on social awareness and task recovery in dynamic environments, where humanoid agents are placed along task trajectories.
2 RELATED WORK
Vision Vision-Language-Acci Model for Navigation. Recent studies leverage multi-modal large mod- els as pretrained backbones for navigation, aiming to use their inherent commonsense knowledge to enhance performance. A common approach formulates navigation actions text, treating the task as next-token prediction within LLMs Zheng et al. (2024); Zhang et al. (2024; 2025a); Gao et al. (2025); Wei al. (2025); Wang et al. (2025d). Others, such as RoboPoint Yuan et al. (2025)
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and NaviMaster Luo al. (2025), frame navigation pixel grounding but still require additional modules for execution. End-to-end methods like UniVLA Bu et al. (2025) and TrackVLA Wang et al. (2025c) map VLM latent features directly to continuous trajectories, but their synchronized frameworks limit high-frequency decision-making in dynamic environments. While some recent dual-system architectures FigureAl (2025); Shi et al. (2025); Bu et al. (2024) explore slow-fast rea- soning, they focus on tabletop tasks and do not address long- long-horizon planning or cross-building navigation. We propose the first asynchronous dual-system architecture supporting long-horizon instruction following. accurate planning, and navigation in unseen environments.
Visual Navigation Policy Learning. Visual navigation enables reaching explicit goals while per- forming real-time obstacle avoidance. Traditional modular approaches Fox et al. (1997); Kramer & Stachniss (2012); Karaman & Frazzoli (2011); Williams et al. (2015); Zhou et al. (2020) rely on explicit localization and mapping but suffer from compounding errors, latency, and extensive hyperparameter tuning. End-to-end learning- baaeing--asedd methods have been proposed address these issues: GNM Shah et al. (2023a), X-Nav Wang et al. (2025a), RING Eftekhar et al. (2024), and X-Mobility Liu et al. (2024) improve zero-shot generalization across embodiments, while iPlan- Yang al. (2023), ViPlanner Roth et al. (2024), FDM Roth et al. (2025), and S2E He et al. (2025) focus on efficient training and sim-to-real transfer. Image-goal navigation has also been ex- plored by SLING Wasserman al. (2023), ViNT Shah al. (2023b), NoMad Sridhar et al. (2024), and NaviDiffuser Zeng et al. (2025). Our System-1 an RGB-only visual navigation policy condi- tioned on latent goals from VLMs.
METHOD
As illustrated in Figure 2, our framework employs a dual-system design that realizes a synergy between high-level reasoning and low-level action execution. System 2, VLM-based planner, performs global planning by predicting mid-term waypoints in image pixel space, providing spatially grounded targets. System multi-modal goal-conditioned diffusion policy, generates continuous trajectories conditioned on current observations and asynchronous latent features from System 2, enabling robust, real-time control complex environments.
System-2
System-1
History
Noised Trajectory
t+
wall atstaiwell
sraishs, riels.
Trajectory Querles
DIT Block
Multi-head Attention
VIT
QwenVL-78
Self Attention
Multi-head Cross Attention
K.
Queries
Q-former
Feedforward
Conditioning
Goal
Slow
View Adjust! Pixel Goal
Latent Goal
Fast
Trajectory
Figure 2: Overview of DualVLN. System 2 takes as input a sequence egocentric images and the instruction to predict either adju-adjustment actions 2D pixel coordinate within the image for the next navigation waypoint. System then takes as input both the latent goal embeddings and high-frequent RGB inputs, then generates continuous trajectories for the robot to follow through diffusion-based policy.
3.1 SYSTEM 2: VLM-BASED PIXEL-GOAL GROUNDING WITH SELF-DIRECTED VIEW ADJUSTMENT.
System integrates high-level pixel-goal grounding with self-directed view adjustment in iterative process. At each navigation step, the agent observes the current RGB frame and history, decides whether to adjust its view or output a pixel goal. This ensures that pixel goal predictions are based on informative perspectives, handling occlusions and challenging viewpoints.
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Farthest Pixel Goal Grounding. We build goal planning module upon Owen-VL-2.55 Bai et al. (2025), a strong open-source vision- lasoonlanguuae model capable of spatial grounding in terms of image pixel coordinates. To adapt Qwen- Dw-2-VL-225 for ssioo-and-languaas navigation (VLN), we formulate high-level planning as a farthest pixel goal grounding problem. The model takes as input sequence of egocentric RGB images along with the language instruction, and predicts 2-1) coordinates within the image corresponding to the next preferred navigation waypoint. To generate training samples, we project the agent's 3-D trajectory onto the 2-D egocentric observations and measure the visibility from the agent's position. Specifically, before projecting the trajectory, we use the depth map together with the camera-poin distance identify which points fall within the visible region of the current view. Any trajectory point whose distance exceeds the corresponding depth value is treated as occluded and discarded. Based on this projection, we segment the original VLN-CE trajectories into pixel goal grounding samples.
Self-Directed View Adjustment. Projecting a 3-D trajectory onto 2-D pixel coordinates can be problematic. If the agent's viewpoint is too high, points on the floor may be occluded, while arti- ficially lifting these points creates depth ambiguity, making it unclear where the actual target lies. Moreover, if the agent is facing the wrong direction, the next waypoint may lie outside the cur- rent field of view. Drawing inspiration from human navigation behavior- people often look around and lower their gaze to the floor before selecting the next waypoint- System autonomously decides when to scan the environment and adjust the camera angle, using discrete actions such as Turn Lefh/Right 15", Look Up/Down 15,, actively seeking informative perspectives before predict- ing the next pixel goal.
3.2 SYSTEM 1: DIFFUSION TRANSFORMER POLICY WITH MULTIMODAL CONDITIONING
Latent Goal Representation. After System 2 autoregressively generates the next pixel goal, the model naturally produces a context feature sequence X encompassing the language instruction, historical images, current observations, view adjustment actions, and pixel goal information. We then append set of learnable latent queries Z, which are randomly initialized and updated via prompt tuning. Processing the combined sequence [X; Z] through VLM enables Z to attend to and extract task-relevan semantic information from X. The resulting Z' forms the intermediate latent goal representation, which conditions System for precise, low-level trajectory generation.
Multi-Modal Conditioning Diffusion Transformer. System 1 is implemented as a diffusion transformer (DiT) that generates smooth trajectories (32 dense waypoints) for robots follow with two sources of conditions: 1. Low-frequency trajectory latents Z' from System 2. 2. High-frequene RGB inputs. Since the dual-system inference performed asynchronously (Slow System 2, Fast System 1), the latent goal generated time t remains fixed. At time t + k, System I must still interpret this outdated latent goal to update the trajectory accurately, estimating the distance already traveled and adapting to dynamic changes.
To achieve this, System encodes both the RGB features corresponding to the last frame from System 2 at time t and the current observation at time + k. Both images first processed by ViT encoder to extract high-dimensional visual features. These features are then fused across the two time steps using self-attention module. To maintain fast inference, the fused features are further compressed using a Q-Former into a compact set of 32 tokens, which serve as high-frequency visual conditioning for the DIT.
Flow Matching, Given the ground truth trajectory waypoints Xo and the two conditioning signals (trajectory latents Z' and fused RGB tokens F),, each training step we first sample timestep u ~ L((0 1) and a noise vector ~ (0, The noisy trajectory is then defined as:
a diffusion
X = O. Xy + oue,
where on is a decreasing function of and o is an increasing function of u.
The diffusion transformer trained predict the velocity X., of the trajectory at timestep u condi- tioned on Z' and F;
X = fo( u 7' F), where denotes concatenation, fo is the transformer network.
(2)
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The training objective minimizes mean squared error between predicted velocity and true velocity:
Noic [1182 1=
Llow
3.3 IMPLEMENTATION DETAILS.
For System 2, we follow the data recipe of Stream VLNN Wei et al. (2025) and finetune QwenVI. 2.5 (7B) for one epoch. Both the vision encoder and the LLM backbone are fully unfrozen during finetuning. For System 1, we introduce four learnable latent queries appended after the pixel goal prediction in System 2 to extract compact latent goal embeddings. The RGB encoder is implemented using the ViT backbone of DepthAnything AaattnAnthin2-Smmalll V2-Small. We adopt a compact Diffusion Transformer (DiT) design to ensure low-latency inference, with hidden dimension of 384, 12 transformer layers, and attention heads. The latent embedding size is linearly projected from 3584 to 768 before cross- attention with the DIT. More details can be found in Section A.
4 SOCIAL VISION-AND-LANUUAN NAVIGATION BENCHMARK.
Approach
BdV
Yielding to
Simultaneous Hulli-Hummnn Encounter
Intersection Encounter
Figure 3: Typical obstacle avoidance capabilities, including not only situations with single agent but also cases involving multiple humanoids simultaneously.
interactions that pose key challenges to the robot's human-aware
Despite recent progress in generalist navigation models, there lacks benchmark designed to eval- uate model's ability to handle dynamic obstacles while executing task-oriented navigation. This ability critical human-centric environments, where robots must avoid collisions and can return to their original task trajectory after detouring. The VLN-CE benchmarks focus on static layouts, leaving a gap in assessing social awareness and trajectory recovery in dynamic settings.
Benchmark Curation. To highlight the importance of the dual-system design and further ad- vance the development of generalist navigation agents, we extend the classic VLN evaluation from static setting to dynamic scenarios. We introduce Social- VLN, new benchmark built upon R2R- CE Krantz et al. (2020b), by incorporating multiple dynamic agents into the simulation, modeled by humanoids provided in Habitat 3.0 Puig et al. (2023). Instead of letting humanoid agents wander randomly between arbitrary start and goal points, we place them strategically along the ground-truth VLN trajectory. Since most VLN tasks are relatively short-range, this targeted placement greatly in- creases the likelihood of nteractions, creating a challenging and realistic test of social VLN. Social- VLN enables a comprehensive assessment of socially-awe awware obstacle avoidance behaviors in diverse situations, as shown in Figure 3. We also carefully verify each episode to ensure agents do not block the path entirely, so that failures don't reflect just simple physical obstructions.
Building on the standard VIN metrics, we further introduce a Human Collision Rate (HCR) metric to explicitly quantify failures caused by unsafe interactions with dynamic pedestrians. Social-VI.N evaluates not only task completion but also the agent's 's safety awareness in dynamic environments.
Training Data Collection. We also develop a pipeline for collecting dynamic obstacle-avoidance trajectories for training. In each training episode, a human detection sensor is setup to continuously monitored the egocentric view. When the human mask pixel ratio exceeded a predefined threshold, modified A algorithm was triggered to replan collision-fre trajectory. This process generated 763K social navigation episodes across 60 MP3D Chang et al. (2017) scenes, forming a foundational resource for training socially compliant agents.
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5 EXPERIMENTS.
5.1
SIMULATION EXPERIMENTS.
Table 1: Comparison with state-of-the-art methods on VLN-CE R2R and RxR Val-Unseen split. indicates methods using the waypoint predictor from Hong et al. (2022).
Observation
R2R Val-Unseen
RxR Val-Unseen
Method
Pano. Odo. Depth S.RGB NEJ OSt SRT SPL
NEL SR SPL1 nDTW1
HPN+DN" Krantz al. (2021)
6.31 40.0 36.0 34.0
CMA* Hong et al. (2022)
/
6,20 52.0 41.0 36.0
8.76 26.5 22.1
47.0
GridMM Wang et al. (2023a)
v
/
5.11 61.0 49.0 41.0
ETPNav" An et al. (2023)
4.71 65.0
57.0
49.0
5.64 54.7
44.8
61.9
ScaleVLN" Wang et al. (2023b)
4.80
55.0
51.0
InstructNav Long et al. (2024)
6.89
31.0
24.0
R2R-CMTP Chen ct al. (2021)
7.90 38.0
26.4
22.7
LAW Raychaudhuri et al. (2021)
6.83 44.0 35.0
31.0
10.90
8.0
8.0 38.0
CM2 Georgakis et (2022)
7.02 41.5 34.3
27.6
WS-MGMap Chen et al. (2022)
6.28 47.6 38.9
34.3
ETPNav FF Wang
(2024)
5.95 55.8 44.9
30.4
8.79
25.5
18.1
Seq2Seq Krantz al. (2020b)
7.77 37.0 25.0 22.0
12.10
13.9
11.9
30.8
CMA Krantz et al. (2020b)
7.37 40.0 32.0 30.0
NaVid Zhang (2024)
5,47 49.1 37.4 35.9
MapNav Zhang et al. (2025h)
4.93 53.0 39.7 37.2
NaVILA Cheng al. (2025)
5.22 62.5 54.0 49.0
6.77
49.3
44.0
58.8
UniNa Zhang et al. (2025a)
5.58 53.3 47.0 42.7
6.24
48.7
40.9
Stream Wei et al. (2025)
4.98 64.2 56.9 51.9
6,22
52.9
46.0
61.9
DuaIVL.N
4.05 70.7 64.3 58.5
4.58
61.4
51.8
70.0
VIN-CE Benchmark Metrics. We first evaluate on the standard R2R-CE Anderson et (2018) and ExR-CE Ku et al. (2020) benchmarks, both built under the VLN-CE Krantz et al. (2020b) setting using the Habitat simulator. These benchmarks simulate realistic indoor navigation Matterport3D environments, where agents follow natural language instructions under continuous control. All ex- periments are conducted on the validation unseen splits to assess generalization. Following prior work, adopt standard VLN metries: Navigation Error (NE), measuring the final distance the goal; Success Rate (SR), the percentage of episodes where the agent stops within meters of the goal; Oracle Success Rate (OSR), where the closest point along the trajectory is considered; and Success weighted by Path Length (SPL), which penalizes unnecessarily long paths.
VLN-PE Benchmark & Metries. We further evaluate VLN-PE Wang et al. (2025b), a physically realistic VLN platform that simulates robot dynamics and control errors in real-world deployment. We report results on the R2R dataset with the Humanoid Unitree HI robot. In addition the stan- dard VLN metrics above, we further report Trajectory Length (TL), Fall Rate (FR), which mea- sures the frequency of robot falls, and Stuck Rate (StR), the occurrences where the agent is unable to move. These metrics collectively provide comprehensive assessment of both the effectiveness and robustness of the system continuous and physically realistic navigation scenarios.
Result Analysis. As shown in Table 1, we compare DualVLN under the VLN-CE evaluation against three representative categories of baselines: (1) Multi-sensor methods that incorporate panoramic RGB, odometry, and depth (e.g., HPN+DN, CMA, GridMM, ETPNav); (2) VLM-free methods trained on single first-person RGB and depth (e.g., CM2, LAW, WS-MGMap); (3) Video-LLM based methods relying solely single-view RGB (e.g., NaVid. MapNav, NaVILA, Und, AtreaVLNN With only first-person RGB inputs, DualVLN achieves substantial gains over all prior RGB-based approaches, highlighting the strength of our dual-system design.

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Table 2 reports VLN-PE results with the physical locomotion controller. Baselines include Seq2Seq Krantz et al. (2020b), CMA Krantz et al. (2020b), RDP Wang et al. (2025b), and NaVid Zhang et al. (2024). Seq2Seq predicts actions from RGBD inputs with recurrent policy, while CMA adds cross-modal attention with instructions. RDP introduces a Transformer diffu- sion decoder for continuous displacements, and NaVid leverages video-based L.MMs for improved generalization without depth or odometry. Despite not being fine-tuned on VLN-PE trajectories, DuaIVLN surpasses all basclines, including those trained on VLN-PE and VLM-baseed methods.
Table 2: Evaluation Metrics on VLN-PE benchmark with physical locomotion controller, +: model first trained Habitat and fine-tuned on VLN-PE. t: model is trained with data augmentation.
R2R Validation Seen
R2R Validation Unseen
Method
TL
NEI
FRI
StR
OS
SR1
SPLt
TLL
NEJ
FRI
StR
OS
SR1
SPL1
Train
on VLN-PE
CMA
11.13
7.59
23.71
3.19
34.94
21.58
16.10
11.16
7.98
22.64
3.27
33.11
19.15
14.05
CMA+
8.86
7.14
23.56
3.50
36.17
25.84
21.75
8.70
7.26
21.75
3.27
31.40
22.12
18.65
RDP
13.26
6.76
27.51
1.82
38.60
25.08
17.07
12.70
6.72
24.57
3.11
36,9
25.24
17.73
Trans fer Eva luation from VLN-CE
Seq2Seqi
7.80
7.62
20.21
3.04
19.30
15.20
12.79
7.73
7.18
18.04
3.04
22.42
16.48
14.11
CMAi
6.62
7.37
20.06
3.95
18.54
16.11
14.64
6.58
7.09
17.07
3.79
20.86
16.93
15.24
NaVid
7.54
6.20
11.25
0,46
24.32
21.58
17.45
7.12
5.94
8.61
0.45
27.32
22.42
18.58
DualVLN
10.65
4.13
17.78
1.82
62.31
58.97
47.78
10.09
4.66
12.32
2.23
55.9
51.60
42.49
Social- VIN Experiment. We evaluate Dua and StNeam on the Social-VI.N benchmark. Stream VLN is selected as the baseline due to its low action latency, which allows to react to dy- namic obstacles to some extent. As shown in Table 3, both methods experience substantial perfor- mance drops - e.g., the success rate of DualVLN decreases by about 27% and that of Stream VLN by 26% compared to their results on standard VLN tasks highlighting the increased difficulty of Social- VLN setting. DualVLN achieves better task completion performance with obstacle avoid- ance than Stream VLN, Nevertheless, there remains considerable room for improvement on this task. We show some qualitative results in Figure 4.
Table 3: Comparison of DualVLN and VLN on standard R2R VLN and Social- VLN.
R2R
Val-Unseen (VLN)
R2R
Val-Unseen (Social-VLNi
Method
NEL
Ost
SR
SPL1
ost
SRT
SPL1
HCR,
Stream VLN
4.98
64.2
56.9
51.9
6.50
36.3
31.4
29.1
36.4
DuaIVLN
4.05
70.7
64.3
58.5
5.97
41.0
37.2
35.8
35,4
5.2 REAL-WORLD CROSS- EMBODIMNT EXPERIMENTS
Experimental Setup. We perform real-world experiments on wheeled (Turtlebo44, quadruped (Unitree Go2) and humanoid (Unitree GI) robots. All are equipped with Intel RealSense D455 cameras mounted at varying heights and angled downward by 15a. The full model runs on a remote server with an RTX 4090 GPU, occupying 20GB memory. Given a VLN instruction, the robot streams synchronized RGB-D images to a remote server for asynchronous inference with the dual- system model. The server outputs trajectories discrete view adjustment actions, transformed into world coordinates via odometry and tracked with an MPC controller. System 2 exploits KV-cache reuse reduce trajectory token inference from 1.1s to 0.7s, while System generates 32 trajecto- ries in parallel within 0.03s using TensorRT. This asynchronous pipeline ensures a fresh trajectory is always available, yielding smooth, near real-time navigation.
Quantitative Analysis. To quantitatively assess DualVLN's robustness and generalization in real- world settings, we benchmarked it against CMA Krantz al. (2020a), and VL.M-based methods

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Evaluation in Simulation
the diray mor. Them
the
donway and clnvet with the cellew pillew on dulff
VLN Instruction
POV Oiservations
Top Down Map
Collition Paliuars
Real-World Obstacle Avoidance
gF
Wat in diecio of hamaned sobec, and Eaily cas skalf li hasinal
Surovie
Figure 4: Qualitative Results of Social VLN Experiments.
Noripston
80
02
63
Single (Medium)
Hallway
Single (Medium)
(Hard))
CCNA
Sbeamin
DuaYYL
Nattt
mANVILA
mamamiL
Figure 5: Evaluation Metries of Real-Worll Experiments.
NaVid Zhang et al. (2024), NaVILA Cheng et al. (2025), StreamVLN Wei et al. (2025) which outputs discrete actions. Evaluations were conducted across hallway (easy), bedroom (medium), and office (hard, room-to-room) scenarios, with 20 trials scenario per model. Performance was measured using Success Rate (SR) and Navigation Error (NE). Among VLM-basee baselines shown in Figure 6. NaVid struggles with complex task, NaVILA handles long-horizon tasks but often misses the final goal in office scenarios. StreamVLN avoids obstacles in some cases but sacrifices task completion. Our dual-system DualVLN consistently achieves high SR and low NE across both static and dynamic scenarios.
Qualitative Analysis. Please refer the supplement video. We evaluate with diverse real-world scenarios, including office, canteen, street, and convenience store, in a zero-shot setting without scene-specific finetuning. DualVLN select correct pixel goals and produces safe trajectories in cluttered environments, plans smooth paths passing all desired landmarks. and handles staircases and dynamic pedestrians. Moreover, the dual-system performs robustly across different robot platforms despite variations in camera height, vibration, and tracking.
5.3 ABLATION STUDY.
Impact of Explicit Pixel and Latent Goal. To assess the role of different goal representations in conditioning System-1 of DualVLNN, we perform series of ablation studies as shown in Figure 7. trained end-to-
We first consider an alternative design without sequential training, where System end jointly with System 2 and does not rely explicit pixel goals. In this setup (w/o Sys.2 Train), observe that the diffusion policy converges significantly more slowly, and System 2's generalization ability deteriorates. This confirms that decoupled training with intermediate pixel goals is crucial for both efficient learning and preserving the reasoning strength the VLM.
Secondly, during System 1 training stage, remove the explicit pixel-goal text from the context sequence X before appending the latent queries Z. In this case (w/o Pixel Goal), the latent goal features cannot attend explicit pixel-goal information. This leads to clear performance drop,
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Sbalic
Static
Bedreoas
Navid
Nevila
Stream
DONIVEN
black chair. Mowe close
Stati
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Office
Navil
Navila
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affic. iosarte yourat sight rigt the torvand walenay and go near bin. Yor open door your right ece,
Figure 6: Real- World Performance Analysis of VLM--based methods.
Reclooo
God
30
55.2
62.2
56.3
51.5
55.8
50.2
00
4.2
4.7
-1.
344
59%
154
8
Figure 7: Ablation Study on the role of different goal representations in conditioning System- w/o Sys.2 Train means training System 1 & 2 jointly in one-stage without explicit intermediate pixel goals. w/o Pixel Goal means removing the pixel-goal text before appending the latent queries. w/o Latent Goal means using the frozen VLM hidden states of the generated pixel goal.
confirming that explicit pixel goals provide valuable guidance for the diffusion policy while also enhancing interpretability and generalization.
Finally, consider variant where only the last-layer VLM hidden states of the pixel-goal text are used as the conditioning signal for System 1. This setup (w/o Latent Goal) yields weaker perfor- mance. The reason is that, without latent goal queries, System is restricted to passively consuming fixed VLM features rather than learning which hidden states should serve as conditioning. This limits the adaptive information flow from System System 1.
Table 4: Ablation study of different local planner on VIN-PE benchmark with flash controller.
R2R
Validation
R2R
Validation Unseen
Local Planner
TL4
NEJ
OS?
SR1
SPL
TL.
NEL
OST
SR1
SPL1
iPlanner
10.9
4.08
66.26
58.66
49.43
9.58
4.91
55.53
47.07
41.09
NavDP
11.68
3.75
76.44
66.11
56.26
10.18
4.22
67.33
58.72
50.98
System
11.26
3.15
78.42
73.25
64.00
10.08
3.90
69.93
63.62
56.49
System 1 vs. SOTA Point-Goal Navigation Policies. validate the advantage of our dual-system joint training framework, we remove the latent goal and convert the explicit pixel goal into point goal using additional depth information. We then integrate state-of-thh-aat point-goal navigation policies (e.g., iPlanner Yang al. (2023) and NavDP Cai al. (2025)) replace System 1 as the local planner. The results shown in Table 4 demonstrate that, even with oracle depth, such a modular pipeline performs worse than dual-system approach. We attribute this performance gap two key factors: (1) the trajectory distribution gap between those produced by point-goal planners and System 2's training data leads degraded pixel-goal prediction; and (2) System exhibits strong vision-based obstacle-avoidant avoidance behavior which makes it robust to small pixel-goal deviations in the correct direction, maintaining accurate, obstacle-aware trajectories, but not to large or semantically incorrect goals (see Figure 8). In contrast, point-goal is highly sensitive to even minor pixel errors by directly projecting the pixel goal into a world-coordinate point.

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Figure 8: System robust to pixel-goal regression errors that still indicates the correct direction but may place the goal near or on an obstacle. But this robustness does not extend to large or semantically incorrect pixel goals especially when the agent close to the obstacles.
Data Scaling Analysis of System-1. For the data scal- ing of System we observe similar trend with Nav- ila Cheng et al. (2025) and Stream VLN Wei et al. (2025):
eSPL
Performence (3
62
more diverse data consistently improves performance, re-
flecting the data-hungry nature of VLM. In contrast, as
60
shown in Figure 9, System exhibits a different scaling behavior. Since it is designed to be lightweight and fast, and the task itself is relatively simple, even using only 1% of the trajectories collected for System 2 already yields competitive performance. Scaling to 10% leads to near- saturation. Further increasing the data scale to 50% does not bring significant additional gains, indicating that the performance upper bound of System 2 has been reached.
5 10
30
50
Percentage of System 2 Trajectories Used (X)
Figure 9: Data Scaling Results of Sys 1.
Consistency between Pixel Goal and Trajectory. To verify that trajectory prediction are strongly guided by the pixel goal, analyze their consistency by projecting the predicted trajectory points onto the image plane. Using 1000 random samples from DualVL.N models with different success rates the VLN-CE benchmark, we compute two metries: the pixel distance between the projected trajectory and the pixel goal, and their average angular deviation. As shown in Figure 10, most points are concentrated in the lower-left region of the plot, indicating that the trajectories are oriented toward the pixel goal and reach areas near the pixel goal.
THE
Figure 10: Correlation between Predicted Pixel Goal and Trajectory
CONCLUSION
In this work, we presented DualVLN, dual-system vision-language navigation foundation model that decouples high-level semantic grounding from low-level action execution. By combining ex- plicit pixel-grounded waypoints with implicit latent goal representations, DualVLN enables more robust, efficient, and generalizable navigation compared to existing end-to-end and modular ap- proaches. Our dual-system joint training framework bridges the gap between semantic reasoning and motion control, producing smoother trajectories and demonstrating strong performance across diverse environments and tasks. We believe DuaIVLN offers a flexible and scalable foundation for future embodied navigation systems, and we hope it inspires further research toward more general- purpose, multimodal, and world-ardd-e embodied agents.
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7 CONTRIBUTIONS AND ACKNOWLEDGMENTS
Model: Meng Wei, Xiqian Yu, Jiaqi Peng, Wenzhe Cai, Delin Feng, Chenming Zhu
VLN Data Curation: Chenyang Wan, Meng Wei
Simulation & Benchmarking: Meng Wei, Chenyang Wan, Delin Feng, Yuqiang Yang, Wenzhe Cai
Real-world Deployment: Yuqiang Yang, Meng Wei, Jiaqi Peng
Advising: Tai Wang, Jiangmiao Pang, Xihui Liu
Acknowledgments: The research work described in this paper was conducted in the JC STEM Lab of Autonomous Intelligent Systems funded by The Hong Kong Jockey Club Charities Trust. This research is supported by Shanghai Artificial Intelligence Laboratory. This work offers a comprehensive claboration and introduces some model improvements for the Dual-System Vision-Language Navigation (VLN) Foundation Model component within the InternVIAA-N framework. We would like to extend our sincere gratitude to all collaborators for their contributions to Intern VLA-NI InternNav-Tean (2025) and the InternNav codebase, encompassing data collection, model development, simulation, benchmarking, real-robot deployment and open-source efforts: Peizhou Cao, Yilun Chen, Zeyu He, Yifei Huang, Wensi Huang, Hengjie Li, Yu Liu, Dahua Lin, Jingli Lin, Yilin Long, Xiachan Mao, Yu Qiao, Jiawei Qiu, Yuan Shen, Yukai Wang, Hanqing Wang, Liuyí Wang, Xueyuan Wei, Chao Wu, Zhenyu Yang, Jia Zeng, Yiming Zeng, Siqi Zhang, Jingjing Zhang, Shenghan zhang, Shi Zhang, Yuchang Zhang, Hui Zhac, Bowen Zhou, Yuanzhen Zhou, Haoyi Zhu, Shachao Zhu (listed in alphabetical order by their last names).
Contributors (2025)
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SUPPLEMENTARY MATERIAL
A DATA PREPARATION AND TRAINING DETAILS
A.1 SYSTEM 2: QWENV
System 2 is trained predict three types of outputs based the agent's observations and instruc- tions: self-directed view adjustments, pixel-goal grounding, and STOP actions.
Discrete Ground-Truth Action Set 0: STOP, 1: Move forward 25 cm, 2: Turn left 15,, 3: Turn right 15
User Prompt Template
User:
autonomous navigation assistant. Your task is <instruction>, Where should you go next to stay on track? Please output the next waypoint's coordinates in the image. Please output STOP when you have successfully completed the task. These are your historical observations: <history>.
You
Self-Directed View Adjustment When the future trajectory cannot be projected onto the current observation (e.g., the start of consecutive actions), the model predicts upcoming turn actions instead of pixel goals. Maximum of 4 consecutive turn actions are predicted per chunk.
Example Converstion:
User: You are autonomous navigation assistant. <history>, Your current observation is <image>
Assistant: + 4444 + (corresponding to Turn right 60 degrees)
Pixel-Goal Grounding When least one future waypoint is visible in the current observation, the model is supervised to predict the farthest successfully projected waypoint as the pixel goal.
Example Converstion:
User: You are autonomous navigation assistant. <history>. Your current observation is <image>
Assistant: (indicating that the next pixel goal is in view User: Your current observation is <image> (it's optional to take looking down action)
Assistant: 234 447
# Pixel goal text
STOP The last step is supervised to output STOP.
Example Converstion:
User: You autonomous navigation assistant. ... <history>. Your current observation is <image>
Assistant: STOP
Training In Stage training, Qwen is fully finetuned to autonomously produce either turn- action sequences coordinate text stop depending the vision and language context.
We use the AdamW optimizer with an initial learning rate of 2e-5 for full finetuning. Training is conducted with batch size of 128 conversation samples and runs for total of 14,000 steps.

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A.2 SYSTEM 1: DIFFUSION- TRAJECTORY POLICY
Smooth and Resample Discrete Action Waypoints Discrete action waypoints are converted into 32 smooth fixed-interval trajectory waypoints via interpolation.
Extracting Latent Representations from QwenVI. To provide informative latent representa- tions of the pixel goal for System 1, we append four special tokens <TRAJ> the end of the text sequence in the pixel-goal grounding data. For example:
User: <observation t>
Assistant:
User: (look-down observation
Assistant: (234, 447)
These latent queries are inserted into the QwenVL embeddings before the forward pass:
ayputs__mbedde OwenVIModel. embed embed_tokens (input ids)
traj_token__ idx = (input_ids == [TAA__OXENNINGE inputs_embedd [trai_ idx] Latent_queria
hidden_states QwenVLModel. forward (inputs_embedi) pixel_goal_latent
hidden_states [-1] [:, -N_QUERY:
The extracted latent representations are then fed into the DiT-based diffusion policy to generate
smooth, obstacle-aware trajectories:
x Trajectory_Ennodee (gt_ (gt_ree__pse_liee noise_pred DiT (x, timestep, pixel_goal latents)
relative poses
Training Only pixel-goal grounding samples are used for trajectory supervision. QI frozen; only the following modules are trained:
1. Latent Queries: Learnable embeddings extract latent goal representations from frozen QL
2. DiT--ased Diffusion Policy: Generates smooth, obstacle-aware world-coordinate trajec- tories conditioned on latent goal representation.
Stage 2 end-to-end trains the latent representation and the diffusion policy predict obstacle-aware trajectories in parameter-efficient manner. The progressive two-stage training of DualVLN ensures both generalized pixel goal grounding and robust trajectory execution.
We use the AdamW optimizer with an initial learning rate of le-4, a batch size of 128 trajectory sample, and train for total of 15,000 steps.
B ATTENTION MAP ANALYSIS FOR PIXEL-GOAL GROUNDING
To gain more insights into how System 2 (QwenVL) grounds pixel goals, we visualize its attention maps over both the language instructions and the visual inputs, including historical video frames and the current observation. In Figures 11, Attention maps from different transformer layers are presented to illustrate which aspects of the multi-modal context the model attends to when predicting the next pixel goal.
We observe that in the shallower transformer layers, the model primarily attends to general contex- tual and spatial cues such objects, scene layouts, and directional clues in both language and visual tokens. As the transformer layers going deeper, the attention begins to increasingly concentrates on the specific target pixel goal region. This indicates progressive refinement from broad scene and semantic context understanding toward precise, goal-directed pixel localization.
We also notice that in the deepest transformer layers, the model assigns significant attention weight to the STOP token when predicting the next action. This demonstrates that the model integrates cues from both visual and language inputs across all layers make final decision task completion.
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...... Your task GO slighlly to the right and walk the table. You'll into the other right and then
big diadem
past stairs, theg fhe hight. hallway by mirror wait. you
go next to stay on track? Please lie fiext waypoint's coordinates the image. Please output STOP when you have successfully completed the task
Layer
15
24
Pbat Goat imara
Layer
15
24
Your task is to Go straight until you pass all the couches and chairs Stop by the large picture of the bridge Where should next to stay track? Plegse-otfpuu the next waypoint's coordinates in tutte-maaee Please output STOP when you have successfully complehft task
Layer
15
24
Piect Imeegs
Layer
15
Figure 11: Visualization of attention maps when predicting the pixel goal.
3


Reviews:
[{"review_id": "CE9elpUkqQ", "reviewer": "Reviewer_8eJ9", "summary": "This article proposes a dual-system framework called DualVLN for the vision-language navigation (VLN) task. DualVLN employs Qwen2.5-VL as System 2 to encode visual observations and language instructions, predicting pixel-level goals based on the current observation. In addition, System 2 introduces learnable latent queries to enhance its interaction with System 1.\nSystem 1 is a diffusion-based model designed to generate short-horizon, high-frequency, and collision-free trajectories. System 2 is first trained through a pixel-goal grounding task and then fine-tuned on VLN datasets, while System 1 is trained using flow matching.\nExtensive experiments are conducted on multiple benchmarks, including R2R-CE, RxR-CE, VLN-PE, and Social-VLN. DualVLN achieves state-of-the-art performance across all of these benchmarks, and real-world experiments further demonstrate its strong performance compared to existing baselines.", "questions": "1.\tHow is visibility from the agent’s position measured when projecting trajectories onto 2D observations? The authors are encouraged to provide a detailed description of the procedure for generating pixel-grounding training samples.\n2.\tThe authors should offer more explanation on the design of the self-directed view adjustment module, including how this ability is trained and its overall effectiveness.\n3.\tIn the ablation study section, Line 425 references Table X, which does not exist in the article. In fact, Figure 6 illustrates the role of different goal representations, but it is not cited anywhere in the text. The authors are advised to clarify and revise this part for better consistency and readability.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 2, "contribution": 4, "strengths": "1.\tThe proposed dual-system framework is novel for the VLN task, offering a promising approach to handle high-level planning and low-level control separately while allowing joint optimization.\n2.\tThe strong performance across multiple benchmarks demonstrates DualVLN’s remarkable capability and its potential to scale effectively with larger datasets.\n3.\tThe dual-system design significantly enhances control frequency, which is crucial for real-world deployment.\n4.\tReal-world experiments conducted on wheeled, humanoid, and quadruped robots further highlight DualVLN’s strong cross-embodiment generalization ability.", "weaknesses": "1.\tThe authors should provide additional details about the ground-truth data collection process used to train the two systems.\n2.\tMore methodological details should be included, particularly regarding the pixel-goal grounding component and the self-directed view adjustment mechanism.\n3.\tAblation studies and further analyses on the self-directed view adjustment module should be provided to better understand its contribution and effectiveness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UaoMmYZOA2", "reviewer": "Reviewer_r9jt", "summary": "This paper proposes **DualVLN**, a **dual-system foundation model for vision-language navigation (VLN)**. The core insight is to decouple high-level semantic reasoning (System 2) from low-level control (System 1) in a slow–fast, hierarchical architecture.\nSystem 2 (“Ground Slow”) is a **7 B-parameter vision-language model (VLM)** that performs mid-term waypoint planning through **pixel-goal grounding** and **self-directed view adjustment**.\nSystem 1 (“Move Fast”) is a **multi-modal diffusion transformer policy** that conditions on both explicit pixel goals and latent features from System 2, enabling high-frequency (30 Hz) trajectory generation and dynamic obstacle avoidance.\n\nThe paper introduces a new **Social-VLN benchmark** to test navigation under dynamic, human-like obstacles, and evaluates DualVLN extensively across **VLN-CE**, **VLN-PE**, and real-world robotic platforms (wheeled, quadruped, humanoid).\nDualVLN achieves new state-of-the-art performance on VLN-CE and VLN-PE and demonstrates real-time adaptability and strong cross-embodiment generalization.", "questions": "1. Why is a **latent goal** required at all if explicit pixel goals already encode spatial intent?\n2. How does System 1 benefit from the VLM’s latent state? Can you show feature alignment or ablation?\n4. What empirical evidence supports the need for a **“fast” 30 Hz controller** rather than standard synchronous inference?\n5. Is there any analysis showing that the **asynchronous design** contributes more than simply using a smaller local policy?\n6. **System 2 → System 1 Coupling:** How are temporal inconsistencies handled when latent goals become outdated during fast inference? Is there a re-synchronization or interpolation strategy beyond using the last frame?\n7. **Self-Directed View Adjustment:** How is the view-adjustment policy trained—supervised with labeled actions or via reinforcement from coverage gains?\n8. **Benchmark Generalization:** Can Social-VLN agents interact with multiple moving agents simultaneously, or only single humanoid trajectories?\n9. **Failure Modes:** What are common failure cases in dynamic scenes (e.g., oscillation, stuck behaviors)?\n10. **Scalability:** Could System 1 be extended to 3D point-cloud inputs or depth channels without retraining System 2?\n11. **Resource Footprint:** What is the average inference latency per navigation cycle (end-to-end) and GPU memory footprint during deployment?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "**1. Clear and Novel Architectural Concept – Dual-System Slow/Fast Design:**\nDecoupling a slow, deliberative VLM planner from a fast, diffusion-based controller is an elegant and well-motivated solution to the latency and fragmentation issues of end-to-end VLN models.\nThe “Ground Slow, Move Fast” principle is conceptually intuitive and technically grounded, providing a bridge between symbolic reasoning and reactive control.\n\n**2. Solid Methodological Design and Implementation:**\nThe formulation of **pixel-goal grounding** and **latent-goal extraction via learnable queries** is novel.\nSystem 1’s **flow-matching diffusion objective** (Eqns 1–3) and **multi-modal conditioning pipeline** (Q-Former + ViT + DiT) are clearly described and reproducible.\nThe asynchronous inference pipeline (2 Hz for System 2, 30 Hz for System 1) demonstrates engineering maturity and real-time feasibility.\n\n**3. Comprehensive Empirical Evaluation:**\nDualVLN surpasses all prior methods—particularly **StreamVLN** and **NaVILA**—on both **R2R-CE** and **RxR-CE** unseen splits (SR ↑ 64.3%, SPL ↑ 58.5%), and maintains superiority under physical dynamics (VLN-PE).\nZero-shot real-world tests across three robot platforms validate strong sim-to-real transfer.\n\n**4. Introduction of Social-VLN Benchmark:**\nExtending VLN to dynamic, human-populated settings (with humanoid agents) is a meaningful contribution. The proposed **Human Collision Rate (HCR)** metric provides a new perspective on social safety and trajectory recovery.\n\n**5. Careful Ablations and Analysis:**\nAblation studies (Figure 6, Table 4) effectively dissect the roles of pixel goals and latent goals, showing tangible drops when each is removed.\nThe **data-scaling analysis** for System 1 provides insights into data efficiency, demonstrating near-saturation at ≈ 10% of training data.\n\n**6. Strong Real-World Validation:**\nResults on TurtleBot 4, Unitree Go2, and G1 confirm robust transfer and low navigation error, with detailed qualitative analyses (Figure 5).\nFew VLN papers provide this level of real-robot evaluation.\n\n**7. Writing and Clarity:**\nThe paper is clearly organized, with high-quality figures (Figures 1–2, 5–7) that make the dual-system workflow and results intuitive.", "weaknesses": "**1. Conceptual Novelty Is Weak and Superficial.**\nThe “dual-system” framing (slow = planner, fast = controller) has been explored extensively in prior works on hierarchical RL, slow–fast control, and modular VLA models (e.g., Helix 2025, Hi-Robot 2025, RoboPoint 2025). The present paper mostly *repackages* a conventional high-level planner + low-level policy pipeline with new terminology (“Ground Slow, Move Fast”) rather than introducing new algorithmic insights. The design feels more like narrative framing than a fundamental contribution.\n\n**2. Limited Theoretical Insight:**\nWhile the framework presents an appealing “fast–slow” dual-system design, the paper offers limited theoretical grounding for how latent-goal conditioning from the VLM stabilizes or aligns representations within the DiT-based controller. The motivation for using latent queries as a bridge between perception and action remains heuristic rather than principled. A more formal analysis, e.g., how latent conditioning improves stability, credit assignment, or planning coherence, would considerably strengthen the work’s theoretical foundation.\n\nFurthermore, the central problem this paper addresses appears to be the offline–online training gap in modular VLN systems, similar to how ETPNav bridges DuET, ScaleVLN bridges HAMT, GridMM & BEVBert, etc., bridge their methods into the continuous environment through waypoint models and online finetuning. It is unsurprising that decoupling the LLM (planner) and controller (executor), then finetuning the latter for error correction, leads to improved downstream performance. However, this design also highlights a deeper limitation: the “fast” and “slow” systems do not jointly plan toward a shared latent goal. Instead, the fast system functions primarily as an error-corrective compensator for the slow system rather than as a hierarchically consistent planner. This undermines the claimed cognitive analogy to hierarchical reasoning and weakens the novelty of the dual-system framing.\n\n**3. Missing Discussion of the System Mismatch.**\nSystem 2 predicts **explicit pixel goals**, while System 1 consumes **latent goals**. The paper lacks explanation:\n\n* Why the latent goal is preferable to directly using the pixel goal;\n* How consistent or accurate the pixel-goal grounding is;\n* Why System 1 actually benefits from the latent embedding.\n\nAlthough L428-L431 try to explain this, it is far from convincing. To me, the results in Figure 6 and Table 4 show that it is necessary to compensate for the offline training gap for a large system during online inference. The diffusion policy could compensate for such a discrepancy by converting the action space into a continuous trajectory and correcting suboptimal pixel goals (by using a latent goal instead). However, these hypotheses are lacking evidence in the paper; no error analysis for why LLM + iPlanner/ DP are inferior, and what error system 2 corrects.\n\n\n**4. Clarity of Data Generation and Training Pipeline:**\nDetails of **System 2 training data**, the projection from 3D to 2D pixel goals, and the **view-adjustment supervision** are somewhat compressed.\nMore transparency (e.g., dataset size, sample generation rate) would aid reproducibility.\n\n**5. Lack of Real Justification for the “Fast” System.**\nThe argument that System 1 must operate at 30 Hz is not substantiated with latency or performance ablation. The experiments show improved results but do not prove that *asynchrony* itself yields the gains. No evidence is given that the fast controller corrects or compensates for sub-optimal pixel goals.\n\n**6. Incremental Empirical Improvement.**\nAlthough numbers on VLN-CE/VLN-PE slightly surpass baselines, the margin is modest and could easily stem from larger model capacity (7 B QwenVL backbone) or training data rather than the proposed dual-system design.\n\n**7. Limited Analysis and Interpretability.**\nThere is no diagnostic evaluation of the intermediate goals, no visualization of latent features, and no discussion of why the dual representation improves reasoning. The Social-VLN benchmark, while new, does not isolate the contribution of the architecture. It simply provides another testbed.\n\n\n**8. Potential Compute and Accessibility Concerns:**\nAlthough System 1 is lightweight, System 2 uses a 7 B VLM (QwenVL-2.5), which may limit accessibility.\nA discussion on inference cost, scalability to smaller models, or open-source readiness would be beneficial.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cGm4ePmZms", "reviewer": "Reviewer_EsMj", "summary": "This paper proposes a dual-system framework (DualVLN) for VLN, consisting of System 2 and System 1. The authors mention that this framework can leverage the strong reasoning capabilities of VLMs and the fast inference speed of diffusion policies. While the implementation is described comprehensively, some concepts lack clarity. DualVLN is evaluated on multiple benchmarks, including VLN-CE, R2R, RxR, and the proposed Social-VLN benchmark. Results from both benchmark tests and real-world experiments demonstrate the effectiveness of DualVLN.", "questions": "- Please refer to the 'Weaknesses' section for detailed concerns.\n- Providing visualizations of the pixel goal in both synthetic and real-world environments would help readers better understand this concept.\n- The rationale for using a pixel goal instead of directly predicting waypoints relative to the robot. Waypoints may seem more straightforward.\n\nIf the authors can address these concerns, I would be happy to raise my score.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 1, "contribution": 3, "strengths": "- The dual-system design appears well-designed, and the results demonstrate that DualVLN achieves both a high success rate and fast execution speed. \n- DualVLN has been extensively evaluated in both synthetic and real-world environments. \n- The proposed Social-VLN benchmark could benefit the research community by providing more realistic VLN environments.", "weaknesses": "The major weakness of this paper is that many statements and descriptions are unclear or confusing:\n - L173 mentions \"self-directed view adjustment capabilities,\" but no details are provided on how this capability is implemented. It remains unclear how the VLA models are trained to possess this ability or how it is utilized during inference.\n- L83 states that the training of System 1 and System 2 is decoupled. This raises the question of how the \"Trajectories Query\" in Figure 2 is learned within QwenVL. Specifically, what supervision signal is used for the latent trajectories?\n- L425 references a \"Table X,\" which appears to be missing. This table seems crucial for evaluating the effectiveness of pixel goal prediction.\n-  The format of the pixel goal is not specified. Is it represented using special tokens or simple coordinate text?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "owkWXdMYqX", "reviewer": "Reviewer_GZFu", "summary": "The paper presents DualVLN, a dual-system foundation model for vision-language navigation (VLN). The proposed architecture decouples slow, global reasoning from fast, local control. System 2, built upon QwenVL-2.5 (7B), performs low-frequency, image-grounded reasoning to predict mid-term waypoints via pixel goal grounding and latent goal representation. System 1, a lightweight diffusion transformer policy, generates smooth, real-time trajectories conditioned on these goals and high-frequency RGB inputs.\nThis asynchronous slow-fast design enables both high-level reasoning and agile local control. DualVLN achieves state-of-the-art results on VLN-CE and VLN-PE benchmarks and shows strong real-world generalization across multiple robot embodiments. The authors further introduce Social-VLN, a new benchmark evaluating social awareness and dynamic obstacle avoidance.", "questions": "Could the authors provide qualitative analysis or visualizations showing how System 2 grounds the pixel goal (e.g., attention maps or reasoning traces)?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. Novel dual-system design that elegantly separates global reasoning and real-time control, addressing key challenges of latency and fragmented motion in VLN.\n2. Pixel goal grounding is a well-motivated and technically sound intermediate representation, providing interpretable supervision and improving semantic grounding.\n3. Strong empirical results: DualVLN consistently outperforms existing VLM-based and diffusion-policy baselines on VLN-CE, VLN-PE, and real-robot experiments.\n4. Comprehensive evaluation, including the new Social-VLN benchmark, ablation studies, and cross-embodiment real-world tests, demonstrating robustness and scalability.", "weaknesses": "1. The paper frames DualVLN as a reasoning–acting foundation model, but the reasoning component is mostly implicit. System 2 performs spatial grounding and waypoint prediction rather than explicit multi-step or interpretable reasoning, making the “foundation-level reasoning” claim somewhat overstated.\n2. The main novelty lies in the dual-system and asynchronous architecture, which is well-engineered but incremental. Similar slow–fast paradigms have appeared in StreamVLN, Helix, and Hi-Robot; the paper could clarify what new learning principle is introduced beyond system integration.\n3. The Social-VLN benchmark is valuable yet briefly analyzed. Deeper quantitative or behavioral analysis would better support its claimed generalization and practical significance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)