OmniNav: A Unified Framework for Prospective Exploration and Visual-Language Navigation
Authors: Xinda Xue
Junjun Hu
Minghua Luo
Xie Shichao
Jintao Chen
Zixun Xie
Quan Kuichen
GuoWei
Zedong Chu
Mu Xu
Zhengzhou Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: Object-Goal Navigation
Instruct-Goal Navigation
Active Exploration
Policy-Diffusion
Primary area: applications to robotics, autonomy, planning
TL;DR: OmniNav is a unified navigation framework using a fast-slow system and large-scale multi-task training to achieve SOTA performance across diverse embodied tasks.

Abstract: Embodied navigation is a foundational challenge for intelligent robots, demanding the ability to comprehend visual environments, follow natural language instructions, and explore autonomously. However, existing models struggle to provide a unified solution across heterogeneous navigation paradigms, often yielding low success rates and limited generalization. We present OmniNav, a unified framework that handles instruct-goal, object-goal, point-goal navigation, and frontier-based exploration within a single architecture. First, we introduce a lightweight, low-latency policy that predicts continuous-space waypoints (coordinates and orientations) with high accuracy, outperforming action-chunk methods in precision and supporting real-world deployment with control frequencies up to 5 Hz. Second, at the architectural level, OmniNav proposes a fast-slow system design: a fast module performs waypoint generation from relatively short-horizon visual context and subtasks, while a slow module conducts deliberative planning using long-horizon observations and candidate frontiers to select the next subgoal and subtask. This collaboration improves path efficiency and maintains trajectory coherence in exploration and memory-intensive settings. Notably, we find that the primary bottleneck lies not in navigation policy learning per se, but in robust understanding of general instructions and objects. To enhance generalization, we incorporate large-scale general-purpose training datasets including those used for image captioning and referring/grounding into a joint multi-task regimen, which substantially boosts success rates and robustness. Extensive experiments demonstrate state-of-the-art performance across diverse navigation benchmarks, and real-world deployment further validates the approach. OmniNav offers practical insights for embodied navigation and points to a scalable path toward versatile, highly generalizable robotic intelligence.

OMNINAV: A UNIFIED FRAMEWORK FOR PROSPEC- TIVE EXPLORATION AND VISUAL-LANGUAGE NAVI- GATION
Xinda Xue Junjun Hu 2xit Minghua Luo"
Shichao Xie
Jintao Chen Zhengzhou Zhu?
Zixun Xie Kuichen Quan'
Wei Guo
Zedong Chu'
Mu Xu'
Amap, Alibaba Group Peeking University
(xuexinda xxd, hujunjun.bt biji, Luominghua. 1mh, teran. xsc, anyi. com
quankuichen qkc, weisheng. gw, xumu.xm, chuzedong
ABSTRACT
Embodied navigation is foundational challenge for intelligent robots, demanding the ability comprehend visual enviromments, follow natural language instruc- tions, and explore autonomously. However, existing models struggle to provide unified solution across heterogeneous navigation paradigms, often yielding low success rates and limited generalization. We present OmniNav, a unified frame- work that handles instruct-goal, object-goal, point-goal navigation, and frontier- based exploration within a single architecture. First, we introduce a lightweight, low-latency policy that predicts continuous-space waypoints (coordinates and ori- entations) with high accuracy, outperforming action-chunk methods in precision and supporting real-world deployment with control frequencies up to Hz. Sec- ond, at the architectural level, OmniNav proposes fast-slow system design: a fast module performs waypoint generation from relatively short-horizon visual context and subtasks, while a slow module conducts deliberative planning ing long-horizon observations and candidate fronticrs to select the next subgoal and subtask. This collaboration improves path efficiency and maintains trajec- tory coherence in exploration and memory-intensive settings. Notably, we find that the primary bottleneck lies not in navigation policy learning per se, but in robust understanding of general instructions and objects. To enhance generaliza- tion, we incorporate large-scale general purpose training datasets including those used for image captioning and referring/rrondiing into joint multi task regimen, which substantially boosts success rates and robustness. Extensive experiments demonstrate state-of-the-art performance across diverse navigation benchmarks, and real-world deployment further validates the approach. OmniNav offers practi- cal insights for embodied navigation and points to a scalable path toward versatile, highly generalizable robotic intelligence.
INTRODUCTION
Embodied navigation (Gao et al. 2024 Gu et al. 2022) has emerged core problem in embodied intelligence: enabling robots perceive, understand, and explore real-world environments with- out pre-built maps while following natural language instructions. To act reliably in dynamic, par- tially observable environments, an agent must not only ground instantaneous visual inputs but also maintain coherent spatiotemporal memory and perform active exploration. Application demands for real-time responsiveness further increase the requirements for low-latency decision- making and cross-enviromment generalization.
Current research largely revolves around three paradigms: point-goal (Liu et al. 2025b), instruct- goal (Anderson al. 2018 Ku et al. 2020), and object-goal (Yokoyama et al. 2024b Xiang al.
"qqual contribution
PProject lead.
Coorrespnniigg author

---

2025
point-goal tasks are well-specified and straightforward to evaluate but rely explicit coor- dinates rarely available in practice; instruction-gon -ogiloonnnoooooooo aligns with human usage but often generalizes poorly to unseen instructions environments; object-goal is the most practical but requires robust target recognition coupled with efficient path planning, making it the most challenging. Many ex- isting methods remain customized, relying task-specific data, which limits cross-task transfer and the potential for mutual enhancement. Uni-Navid (Zhang et al. 2024a) proposed a VLM-based discrete action predictor unifying usioo-and-language navigation, object-goal navigation, embod- ied question answering (Das et al. 2018), and following (Wang et al., 2025a), but its study of LLM long-horizon planning not sufficiently developed. MTC3D (Zhu al. (025) advances "move to understand" paradigm by coupling frontier exploration with visual localization in single objec- tive, yet requires constructing 3D object coordinates, leading to deployment complexity. Although recent Video-II.Ms (Wei et al. [025 Qi et al., 2025 Zhang et al. 2024b) and VIAS (Sapkota)
iet al. 2025| Zitkovich et al. 2023] Ma et al. (2024) integrate vision, language, and action predic- tion end to end, they still face bottlenecks in streaming video input, long-contest management, and low-latency inference: discretized action modeling sacrifices precision and flexibility; constrained LLM call frequency and frequent context resets lead to deployment difficulties; besides, in practice, the dominant failure mode often stems from inadequate understanding of generic instructions and open-vocabulary objects rather than policy learning itself. These gaps call for unified, efficient framework that balances seagsshoot-hhrizzon reasoning with real-time responsiveness.
We present OmniNav, a unified embodied navigation framework that concurrently covers instruct- goal, object-goal, point-goal. and frontier-basee based exploration within a single architecture. Inspired by dual-system theory (Figure, 2024, Black al., 2023), OmniNav coordinates a fast-slow sys- tem (Black et al. 2025): ): fast system reacts to comparatively short-horizon perception and current gegh-precision waypoints (coordinates and orientations) support low- latency control up to 5 Hz; a slow system deliberates over long-horizon observations and frontier cues, leveraging a VLM's chain-of-thought (Wei et al. 2022; Chen al., 2025e] Liu et al., 2025a) to decompose complex goals and select the next subgoal and subtask. The two coupled through a central memory module that uses a key-value (KV) cache to provide essential spatiotemporal context, yielding decisions that are both locally agile and globally consistent.
tasks or subtasks, generating
OmniNav addresses the triad of real-time operation, fast-slow collaboration, and generalization. A lightweight flow-matching policy (Bjorek al. (023) avoids the precision degradation and la- tency accumulation inherent to action discretization; fast-slow collaboration ensures exploration efficiency and trajectory coherence in long-memory scenarios; more importantly, training unifies large-scale generic vasion-language data (captioning, referring/grounding, etc.) with multiple nav- igation tasks, significantly strengthening instruction following and vpecbvocab object percep- tion to improve success rates and robustness. Our contributions are threefold:
A unified architecture that, under a single training framework and policy, supports multiple goal modalities (point, object, and instruction) as well as trontier-based exploration;
An end-to-end fast-slow coordination with central memory that reconciles low-latency control and high-level deliberation;
A principled strategy to incorporate generic llsion-language data into joint training, sys- tematically improving cross-task and cross-environment generalization.
Extensive experiments set state-of-the-e the----rttte--tttt results across multiple navigation benchmarks, with real-robot deployments further validating practicality. We contend that OmniNav charts a scalable path toward multifunctional, highly generalizable embodied navigation systems.
2 RELATED WORKS
Vision Language Models for Navigation Leveraging their powerful generalization capabilities in understanding and planning, Visual Language Models (VLMs) (Chiang et al. 2023 Liu et al. 2023]
Zhu et al.) 2023) have been increasingly applied to the domain of robotic navigation, achieving notable success. Prevailing methods (Dorbala et al. 2022 Zhou et al. 2024b. Long et al.) 2024b) typically employ VLMs process multimodal instructions and directly decode low- actions in an autoregressive manner. However, this paradigm suffers from significant drawbacks: it is prone to compounding errors in sequential prediction and is often hampered by slow inference speeds. In
2

---

contrast, our approach draws inspiration from recent advances in Vision- Laaian-LLnnuggattnnaata (VLA)
models (Zitkovich et al. 2023] Kim et al. 202244 Li et al. 2024). We introduce a novel architecture
that appends wowtmtt policy (Zhao et al., 2024; Chen et al. 2024 Zhou et al. 2024a Hu
et al. 2025 Yang et al. 2025) to a VLMM backbone. This design enables our model to generate entire action trajectories non-autoregressively. leading to substantially improved prediction accuracy and computational efficiency, especially when navigating unseen environments.
Dual-System Design Dual-system architectures have been widely adopted across various domains to meet diverse operational demands, In the realm of Vision-Language- Action (VLA) models, sev- eral works (Bjorck et al. 2025 Bu al., 2025 Ge et al. 2024) Song et al. 2025) have implemented dual-system designs to balance fast control execution and intelligent planning. Inspired by this paradigm and motivated by the specific requirements of embodied navigation, we propose a novel dual-system framework. Our framework consists of two complementary components. The first is fast system, a purely visual, end-to-end policy designed for direct deployment and is highly effective in the majority of navigation scenarios. The second system is specifically engineered for challenging long-horizon tasks, To serve as long-term memory mechanism, we employ a planning strategy that combines frontier-based exploration (Zhu et al., 2025 with images. This approach offers more concise implementation compared to alternative memory structures such as scene graphs (Team) al. 2025) or complex semantic maps (Long al. 2024a). These alternatives memory structures can also be implementations for the slow system. The idea stays the same: the slow system is re- sponsible for global planning, while the fast system handles local execution. This synergistic design has proven its superiority by achieving State-of-the-Ae performance on multiple benchmarks.
booteerr-baeed Navigation Recent studies on exploration and navigation adopt different strategies for selecting informative targets in unknown environments. GOAT (Chang et al., (2023) and its benchmark GOAT-Bench (Khanna et al., 2024) study lifelong navigation (Ren et al.) (2225) and object search using an object- -iteettinstance memory and frontier exploration. Similarly, MTU3D (Zhu et al. 2025) keep an object-goal memory built from 3D point clouds and semantic segmentation, and combine this with frontier exploration. A different group of methods uses non-semantic frontier exploration (Chang et al.) 2023: Sakamoto et al.) 2024; Nayak et al. 2025). where the next target is usually just the closest frontier, sometimes adjusted by simple heuristics such as distance-heading scores. OmniNav instead uses a semantics- and reasoning- aware frontier selection: it links each frontier to its egocentric images, then uses explicit chain-of -f-tuhuggttt reasoning over these views to decide which frontier is more informative or promising for the current task.
APPROACH
Multimodal Input tokenizations To handle all four task types through unified interface, text, coordinates, and visual history are converted into set of discrete tokens consumable by a Large- Language Model (LLM) see Fig. [ We use Qwen2.5-VLL33--Intte
(Bai et al., 2025) as the base model and extend it with coordinate modality. During streaming inference, key-value (KV) cache is maintained to reduce latency. Text tokens: Derived from natural- natural-language task descriptions, object category labels, and point-goal commands, sequence. Coordinate tokens: Candidate search regions are represented as sets of 2D coordinates and heading angles, sourced from point-goal inputs or subgoal positions generated by the slow system. These coordinates are processed via an MLP to dense embeddings that serve as coordinate tokens. Image tokens: The central memory maintains ring buffer of pose-stamped images. For the fast system, it spatiotemporally samples from the historical image sequence, maintaining maximum number of images (e.g.. 20 frames). For the slow system, it samples images from the spatiotemporal neighborhood of candidate frontiers. All images are encoded with a ViT to produce image tokens.
all converted into standardized instruction
Fast Thinking System OmniNav operates at high frequency, designed to execute either subtasks provided by a slow system or end-to-end multi-task navigation, as shown in Fig. D It parallelly outputs a sequence of 5 continuous-space waypoints, WIa+ E H x5 with H = 5. We formulate waypoint prediction as conditional diffusion generation task. The input coordinates are first em- bedded by an MLP and then encoded together with the images and texts by the VLM. The VLM performs deep fusion over these features, and the resulting fused features are used conditions for the diffusion model, guiding waypoint generation. This design preserves the VLM's semantic understanding while enabling rich interactions between language-guides context and the waypoint,
3

---

Tokenization
Text Tokens:
Fast Thinking System
Low-level Waypoints
Coordinates
BEV
Prompt
2. 02) 02) 0.31
(Kss v.).
<Object-Gocl
"Search for on instance of dichecsher within this space"
Sub-goal Coordinates
Unexplored Frontiers
Tokerss
<Insfruct-Gool
"Go down the
VLM
Cond
Diffusion Palicy Head
up
stairs ... ...Wiit
sink.
Sampling
VIT
<Point-Gool>
"Given past rosordinates and <mages predict the next 5 waypeints."
Image
Spatiotemporal
sompled images
Tokens
Image
KV coche
VLM Feats
Head
Nesie
Slow Thinking System
Thinking
Prompt
Analyze: The model analyzes the envirenmett
<Peint Goah "Given past ciroge. predict next wrypeints"
Low-level Waypoints
defermine the next action.
the <Goab, then stop.
VLM
Casie (Terget Not Visiblej: From frontier, the
optinal high-level subgoai cooordinates.
VLM
Head
[(1 2. 15",0.23.
Vision Input
Highilevel subgoal) found rcoordinate.
(Target Found Memory): The torget
(1. 43',0.3)
Coordinates
Global Planning Stage
Planning Stage xN
VLM
The VLMS the fost and slow systems shore the some parometers.
Figure 1: The fast system can independently handle multi-task navigation, using the VLM backbone and flow-matching policy to rapidly generate waypoints. Building on this, a slow thinking module integrated to enable long-term memory and planning: it constructs long-range spatial and semantic memory using frontiers and images, and provides subgoal cues. The collaboration between the slow and fast proceeds follows: the slow system uses frontiers memory generate high-level subgoals, once a subgoal determined, the fast system takes over and progressively produces low- level waypoint sequences, ultimately reaching the target.
leading to robust instruction following. Compared with conventional autoregressive methods, the policy head achieves speedup and, with history of 20 frames, supports inference rate of 5 Hz for real-time closed-loop control. At the same time, produces smoother and more precise trajectories.
We employ a variant of the Denoising Transformer (DiT) (Peebles & Xie 2023 to model waypoint sequences. The policy network consists of self-attention blocks that operate noised tokens to capture temporal and spatial dependencies within the waypoint sequence, and cross-attention blocks that attend to the vision-language context O OvLas The output is sequence of И spatial- temporal waypoints w/" E ER5, 1,..., H, each encoding:
w
(+( (0) sin (()) cos g(0) c(())
(1)
where (xx ()) denotes the 2D position, (10) is the orientation (represented via sine-cosine em- bedding to avoid discontinuity at \/ T)) and c!()) {0, 1] is binary completion flag indicating whether the "arrive" command should be triggered at the i-th waypoint.
Conditional flow matching policy is employed (Lipman al. 2022). Given a ground-truth waypoint sequence WES+H. noise e ~ (O, I), and a time parameter [0, 1]. the input is constructed as:
w W = TWE1+H + (1-T)E,
(2)
and the policy æ is trained to estimate the denoising residual WeitH by minimizing:
E.e [[ w -(E -W
(3)
At inference, waypoints are generated via steps of Euler integration. Starting from initial noise wha ~ (O I), we iteratively refine the sequence:
w T+AT = w + + (OVLM, whaHH), AT Wi++
(4)
5
S'
with increasing from to 1. The final denoised output wi Wi+t serves as the predicted waypoints.
4

---

Slow Thinking System The slow thinking system is the deliberative planning module for hierarchi- cal active exploration. Its core responsibilities are twofold: when the target appears in the current or historical field of view, it can quickly localize the target and generate the coordinates of the subgoal that drive the fast system to progressively approach it, when the target is not observed, it selects subgoal position with strong semantie relevance to the target explore next. This process demands both exploration and environmental understanding. The former requires the model to navigate and discover the environment, while the latter involves a core task: predicting the spatial coordinates of the target based on input camera poses and images, this grounds the model's semantic understanding concrete, geometric output.
In the fast-slow system collaboration, the fast system is not just low-level controller following preset coordinates or smoothing a pre-planned path. It must constantly use raw visual input to move toward its subgoal. For example, if it gets coordinate from the slow system but a wall blocks the straight-line path, the fast system must use visual cues to find path that avoids the obstacle. If gets target coordinate from object memory, it can adjust its final pose based on what it currently sees, such as stopping precisely at the left, right, or center of the object. By continuously updating and refining its waypoints while moving, the fast system can reach targets more accurately.
Frontier (Zhu et al. 2025) is employed to guide the active exploration. We maintain a 3D occupancy map, which categorizes each region explored or unknown, and frontiers are then identified as the boundary points between explored and unknown regions. In addition, to comprehend past temporal and spatial information, Zhou et al. (023). This repository archives a history of observations, storing the visual data and corresponding pose information (coordinates and orientations) after every executed action. We then design a sampling strategy that connects this historical context to future exploration by collecting all historical images captured near the agent's 's current location. It then evaluates each frontier by iterating through these images, sampling the one whose original capture viewpoint is most suitably aligned with the frontier's spatial coordinates. This image thereby becomes a visual proxy for that frontier.
construct memory bank (Zhu al., 2025 Olton 1977; Xu al. [024]
During frontier selection, the model engages in comprehensive spatial and content reasoning, reflect- ing its capability to actively explore unknown environments and propose subgoal locations related to the target object. For example, when searching for a toilet, it prioritizes exploration locations in the bathroom; when searching for television, it seeks locations associated with the living room. Moreover, if the target object appears in memory or within the current view, it outputs the target's existing location. We incorporate explicit chain-of-thought (CoT) (Lin al. 2025 Wang et al.) 2025b Zhao et al., 2025) reasoning into the slow system's prediction process to enable transparent process expression, achieving interpretability and self-correction. This also allows for richer textual outputs that strengthen the model's grasp of logic and improve complex reasoning performance. Fig 2 illustrates the slow system's 's reasoning process.
DATA AND TRAINING
Data in the embodied domain typically organized as a data pyramid (Bjorck et al. 2025). with internet data and human video data at the bottom, simulation or synthetic data in the middle, and real-robot data at the top. Our dataset follows this rich composition as well, including general web data, simulation data, and a very small amount of real-robot data detailed in Fig.
4.1 GENERAL DATASET
It is found in our experiment that models can learn the navigation paradigm relatively easily, whereas general- general-purpose purpose capabilities remain challenging. To strengthen these abilities, we extended the train- ing data with broad-coverage general-purpost datasets. General-purpose datasets (general QA, im- age captioning, OCR, chart understanding, coding, and math) complement Vision-and- Lasinn-and-Langugee Navigation (VLN) by supplying foundational skills in language understanding. visual semantics, OCR recognition, structured reasoning, and algorithmic planning. These capabilities improve in- struction comprehension, path planning, and overall robustness. They also introduce commonsense and functional priors, such as "bath towels are commonly found in bathrooms". We draw on the
5

---

corpet,
The connects mi ltale
nooes making
Hieterion Frone Anchys Tea. flom
becisen fahennle/rrmmter po he 3, leade fowards on open oreo
wooden there sendon
right Thes oppazns oorher quee space where llseihood finding Seseral doans visille direhhr which oslly indiretek abillary looding bedreems muitipl privtte reies Decision fremter i (33) 1.1....
opening
point
Secision Rathoralicll The batt room. Prenrer point [-4.B
1.9 would loot me fur
usiiicly
Frontier
Task:Find the bathtub
leads towords a lo see centrol crea which based or the farphtan layout
Histonical Frome Andlysis
Des frontier 136. 244
field
cleenly see large,
object
hase shope and posit
dicette it bathtub..
becision Rattonale: The most
pirteroog From
hostooed
direct and efficient strategy is
Hallway
to identify the precise location
bedreon) and anothor room the right Decisin Rottonaler Freeter point (2. -3.7 lead Whil
hal hallway
the torget object and stop
furthar explonstion...
oppoain
Decision
objectt
33, in conteatt loodr directy the now an ere one dready abservad the mvrnge and sak Key Tects garyeen typically
dikk
bathrub..
Deciseee 1233 0.8)
Reasoning process by the slow system for exploration. For the "find the bathtub" task, the model reasons over the frontier set using memory and semantic priors, iteratively generating subgoals for the next exploration.
Figure 2:
1. Navigation Task Data (400w) (b) Point (100))
3. General MLLM Data (500w)
(n) Object (500g)
-ll33-3
Ilistory Frimus
Current
Caprian(47 2%))
40002014
LC0002017
GeneralQA
Caption
GenerolQA(33.9%)
0CR(7.8%)
your suroundings
Mission: Daned the above information, plase prolic the positin coondraates of next frumes Answer (0.0129.1 (832) waypoints
Chart Coding &Math
OCR
Coding Math(6.4%)
111-
waypoinns
Char(44.7%)
(c) Instruction G5al(75w)
(d) Exploration((Ow)
History Frames
Current view
4. Grounding Referring Data
(300w)
(a) Grounding Data
MiseoncMove banch, Candidase thontion:
Missior: Walk left of tablee and cheims.
(9:36, (-L.3,,, 0.19). 24, Answer bench viable, chose best trontie: 6..33.0.19
Walk toward the dining room table and chaies
Embodied O&A Datalzow)
Labels: dog,
Labels: bowe,, Blox. [314, 635, 13......
Bbox: 47.62,47.322
Referring Data
Capl: get very center with sed cost on chair.
What coloe tuble is on the left side of the cabinct?
Cr doserbe the ravigation trajoctary the robut.
Cap2: brow straight
ligha brows
A: Wirk down the hall and turn righs.
Wilk pat the hed and step deveway.
Figure 3: Data composition overview. Four data types are used for training: Navigation task data, Embodied Q&& data, General MILMM data and Grounding and referring data.
open-source MAmmoTH-VL corpus (Guo et al. 2024 and subsampled examples to expand our general capabilities; the composition ratios shown in Fig.
We further incorporated grounding and referring data to more reliably ground linguistic targets and relations to concrete pixels, instances, and locations in the scene, thereby yielding policies that are more interpretable, robust, and generalizable. This includes fine-grained language-to-tee tt-tget map- ping-fo for example, precisely localizing language like "red sofa," "door with handle." "the second chair" to the correct image regions to support instance-level disambiguation. also en- compasses spatial and relational understanding, such learning to ground spatial prepositions and

---

relations ("to the left/in front of/at the corner"), and extracting actionable turning and stopping cues from relational descriptions like "the door next to the painting" or "after passing the hallway, turn right at the first intersection." The referring and grounding data are soureed from RefCOCO ries (Chen et al. 2025b) and Objects365 (Shao et al. 2019).
4.2 MULTI-TASK NAVIGATION DATASET
Point-goal data. We use the open-source Citywalker corpus (Liu et al. 2025b), which consists of first-person city-walking videos collected from public YouTube channels. Each trajectory comprises a single forward-view sequence. Long videos are segmented into 2-minute clips, then the camera pose for every frame are recovered by DPVO (Deep Patch Visual Odometry) (Teed et al. 2023).
Instruct-goal data. Under Habitat's VLN-CE (continuous environment) setting and Matterport3D scens (Chang et al. 2017). based on the public instruction- path pairs from R2R (Anderson et al., 2018) and RxR (Ku et al. 2020). For each trajectory, we store a panoramic sequence composed of three-views (front, left, right), the action and waypoint sequences, and the natural-language instruc- tion.
Object-goal data. Specifically, open-vocabulan object navigation (OVON) data (Yokoyama al. 2024b). In Mabitat-Matterport33 (HM3D) (Ramakrishnan et al. (021)) scenes, randomly sample (start pose, target object category) pairs. The built-in shortest-pan path navigation algorithm is used to move the agent to the vicinity of the target. The same content recorded as the instruct-goal data for each trajectory.
Object-goal (with frontier-based exploration) data. At each step the agent updates its occupancy map and visible region to identify all current frontiers. A policy then selects one coontee--faorrinn the shortest path while introducing limited randomness- the current subgoal. Once the target object is found, the exploration episode terminates successfully, yielding a trajectory (Zhu et al.] 2025). Each trajectory record includes a single forward-view frame sequence, a unexplored frontiers sequence and a natural-language instruction.
Embodied QA. ScanQA (Azuma al. 2022) focuses real-world indoor 3D scene understanding. with QA pairs centered on object locations, attributes, and spatial relations. R2R-EnvDrop (Tan al. 2019) addresses continuous visual navigation: scenes are from Matterport3D. trajectories are from R2R-CE, and the navigation instruction-trapie trajectory pairs are recast into QA format to strengthen alignment between linguistic expressions and visual observations.
Navigation data Process.Each action step corresponds to 3D continuous pose, described jointly by position and orientation. Taking the agent's current front-view coordinate frame as the origin, all other trajectory points are transformed into this local frame. Then, we project each 3D pose onto the ground plane, keeping only the planar coordinates (z,y) and the heading angle (0), and additionally attach "arrival" flag to each step. In this way, each trajectory point is ultimately represented as a quadruple (x, y, 0, arrive), which serves as the primary target for waypoint op- timization. Under this abstraction, since the underlying simulator ensure that any valid 2D path passing through a stair region can be realized as a 3D trajectory across floors, this representation naturally supports multi-floor navigation in HM3D: moving from one floor to another is encoded as following sequence of 2D poses from the stair entrance to the stair exit, while the vertical motion handled implicitly by the simulator. At the same time, the agent always receives 3D visual obser- vations with full geometric information, so the stair structure and floor changes are reflected in the visual features.
4.3 DISCRETE AND CONTINUOUS JOINT TRAINING
We adopt a two-stage training paradigm to balance language -vision semantics and continuous con- trol. In Stage 1, we use autoregressive (AR) objective to predict discrete variables (e.g., navigation action chunks, general-purpon
semantic data, Embodied QA, grounding and referring data; see the four data types in Fig. 3). to achieve alignment between language-vision and action. In Stage 2, we attach a flow-mater matching policy to the shared backbone to predict continuous waypoints, and perform joint training by including 20% of the Stage-1 discrete data to prevent degradation of the base VLM during ciainuous-contrul fine-tune tuning. The continuous waypoint coordinates are normalized using min-max normalization to ensure stable training and better convergence.
7

---

A joint training scheme is particularly critical for Vision Vision-and- and-Language Navigation (VLN), which requires strong general knowledge and semantic understanding, leading to substantial improvements in success rates in open environments. Stage I is trained with 96 NVIDIA H20 GPUs for 120 hours, and Stage is trained with 64 NVIDIA H20 GPUs for 48 hours with lower learning rate.
EXPERIMENTS
Table 1: Main comparison with prior methods the Val-Unseen split of R2R-CE and RxR-CE.
Method
Observation
R2R-CE
Val-Unseen
RxR-CE Val-Unseen
S.RGB
Pano.
Depth
Odo.
NEJ
Ost
SR
SPL1
NEL
SR1
SPL1
HPN+DN*
Krantz et al.
2021
6.31
40.0
36.0
CMA (Horg
6.20
52.0
41.0
36.0
8.76
26.5
22.1
Sim2Sim
Kraniz Lec
2022
6.07
52.0
43,0
36.0
GridMM
Wany el al. 20255
5.11
61.0
49.0
41.0
Dream Walker
Waat
2023a
5.53
59.0
49.0
Rebom An et al
X122
5.40
57.0
50.0
46.0
5,98
48.6
42.0
ETPNay el
2124
4,71
65.0
57.0
49.1
5.64
54.7
44.8
HNR WWng al. 2024]
4.42
67.0
61.0
AG-CMTP (Chen al.
2021
7.90
39.0
23.0
19.0
R2R R2R-CMTI' (Chee CC el ai
2021
7,90
38.0
26.0
22.0
InstructNav (Long ct
20:40
6.89
31.0
LAW (Raychaudhun
6.83
44.0
35.0
10.90
8.0
CM2 GGorgaais a 2022
7.02
41.0
34.0
27.0
WS-MGMap (Clin et al.
6.28
47.0
38.0
AO-Planner (Chen
5.55
59.0
47.0
7,06
43.3
30.5
Seq2Seq (Kraniz cl 2020
7.77
37.0
25.0
22.11
12.10
13.9
11.9
CMA (Kraniz et al. 2020
7.37
32.0
NaVid Zhang
20241
5.47
49.0
37.0
35.0
Uni-NaVid Zhans or m.
2024a
5.58
53.5
47.0
42.7
6.24
48.7
40,9
NaVILA Cheng al.
5,22
54.0
44.0
Stream
4.98
64.2
56.9
51.9
6.22
52.9
46.0
ConretNav oo ar.
4.24
67.5
65.1
62.3
4.09
69.3
63.3
OmniNNa((//o policy-head)
4.36
65.0
59.8
57.5
3.87
64.1
53.9
OmniNav
3.74
3.77
62.0
Table 2: Evaluation of object-goal navigation on HM3D-OVON, where * indicates OmniNav with slow thinking system.
Method
Observation
Val-Seen
Val-SeeenSSyonyym
Val-Unseen
S.RGB
Depth
Odo.
SR
SPL1
SR1
SPL1
SRi
SPL."
BC
11.1
4.5
9.9
3.8
5.4
1.9
DAgger
11.1
4.5
9.9
3.8
5.4
1.9
18.1
9.4
10.2
4.7
DAgRL
21.2
29.4
18.3
7.9
BCRL
39.2
18.7
27.8
11.7
18.6
7.5
VLFM Yokoyama ct al. 2024a
V
v
35.2
18.6
32.4
17.3
35.2
19.6
DAgRI Yokoyama 2024h
V
38.5
21.1
39.0
21.4
37.1
19.8
Uni-Na Vid hang 202440
41.3
21.1
43.9
19.8
MTU3D ZZhu
55.0
23.6
45.0
14.7
40.8
12.1
OmniNav
46.6
23.3
50.4
28.5
43.5
27.3
OmniNav"(w/ cot)
v
56.1
30.0
68.6
38.8
59.2
33.2
Metries We evaluate navigation performance using success rate (SR), oracle success rate (OS), suc- cess weighted by path length (SPL), and navigation error (NE). Our evaluation protocol is consistent with prior work (Zhang et al. 2024a Zhu et al. 2025) and follows standard practice.
Instruct goal As shown in Table I On the R2R-CE and RxR-CE benchmarks, compare model against all relevant competitors, including both discrete and continuous prediction methods. Notably, using only its fast system and pure RGB inputs, OmniNav achieves state-of-the of-the-art success rates on both benchmarks. It surpasses the previous leading model by improving the success rate by 4.4% R2R-CE and 4.3% RxR-CE.
Object goal To further validate open vocabulary generalization, we also compare OmniNav with prior methods on the HM3D-OVON benchmark. As shown in Table 2 under purely visual inputs,
8

---

OmniNav already surpasses the best existing approach by 2.7%. However, given that the OVON task demands
and global planning, the limitations of a purely reactive fast system -ucc as getting trapped in local loops and exhibiting poor map coverage become particularly pronouneed. Therefore, we incorporate the slow system integrated with frontier-based reasoning, which neces- sitates the of depth and odometry information to build and maintain an occupation map. This augmentation equips the agent with global spatial awareness and the capacity for proactive explo- ration, ultimately leading to superior overall performance((xceeding the strongest prior method by 18.4%).
become
Point goal We compare point-goal performance on the CityWalker benchmark, state-of-the-ar point-goal method that supports outdoor navigation scenarios. City Walker adopts MAOE (Mean Average Orientation Error) as an open-set evaluation metric. On this open-set metric, our approach still outperforms the benchmark method (OmniNav:1 1.53% vs City 1
Ablation Study The independent and synergistic contributions of the four key components poli- cies are evaluated as show in Table [able[andd and T 1) Fast system: autoregressively action chunks generation vs. continuous waypoints generation by flow-matching policy. On R2R-CE, RxR-CE: and OVON benchmarks, the degradation is substantial for action chunks. Their semantic tokens (e.g., left, right) align more easily with language, making them suitable for the first-stage training. However, because action chunks are coarse- coarse-grained motion control, continuous waypoints are better suited for fine-grained control. 2) Slow system (planning with frontier and long-term visual mem- ory). We primarily compare with/without the slow system on long-horizon exploration in OVON and find the largest improvements here. Once previously explored areas are recorded, the agent reduces redundant exploration and improves efficiency. Moreover, decomposing active exploration into subgoals (e.g.. "go to the bedroom first") and letting the fast system quickly approach each subgoal forms a hierarchical "plan-exeee"" "xenutxe loop, which better matches human reasoning and be- With the slow system enabled, adding the general-purpon datasets yields further stable gains. 4) CoT (explicit thain-of-thought outputs). Using CoTT makes the basis for subgoal selection in the slow system transparent, enabling process-level self-check and correction. It reduces cumulative errors in long chains and complex semantic tasks, producing stable improvements. When all four en- abled, performance is best: the slow system provides semantically plausible long-horizon subgoals; the policy head executes with high precision and low latency; general data injects commonsense and language- vision
havior in unfamiliar environments. 3) General data (general MLLM and anferring//rounding
maguuge--isionn alignment; and CoT provides auditable processes and self- correction.
Table 3: Ablation study on HM3D-OVON Val-Unseen
Method
Module
Val-
policy-head
slow-system
general data
COT
SR
SPL
OmniNav
35.3
22.1
43.5
27.3
OmniNav*
55.9
30.7
57.7
32.9
59.2
33.2
Real-world deployment In real-robot deployment see Fig. 4 we deploy the fast system component of the model architecture-comppeee 3090 GPU. The history buffer holds up to 20 front-view frames with at a resolution of 120x 106, while the current input is tri-view at 480x426. The system runs at over 5 Hz. For all tasks, it outputs waypoints, which are then fed into the onboard speed control module, which, based on the current speed and maximum accelcration, generates a set of candidate speeds. It then selects the optimal speed to approach the waypoint (selection criteria: minimize speed changes to maintain high speed as much as possible, and be closest to the target waypoint).
the VLM and the policy head on a cloud server with an RTX
Deploying the full slow system in real-world settings requires additional engineering, such as robust real-time integration with LiDAR/depth estimation. This paper mainly focuses on validating the effectiveness of the dual-system collaboration framework in terms of navigation performance and behavior. A full physical deployment of the complete slow system, and systematic optimization of its

---

performance under real-world constraints (including detailed latency frequency trade-off analysis), constitutes an important avenue for future research.
Object Goal
2. Point Goal
(Visual-nnfy obstacle avoidance)
(3)
dispenser.
(c) Avoid sofis and people.
(b) Missiuz: Find a wumar weating pink shin..
(c) Avaid inregularly shaped chair legs.
Object Goaf & "Understanding
3. Instruct Goal
(c) Missim:: I want throw away trash. (find trash
(1) Mission: Go into the first
(g) next interncctonn after passing the public
stop
chair.
Figure 4: Real-world deployment. It shows third-person view of the three different navigation tasks which are deployed in a zero-shot setting. The gradient blue arrows indicate the trajectory, and the yellow box marks the target location. Our model demonstrates highly effective navigation performance on the real quadruped robot.
CONCLUSION & FUTURE WORKS
Conclusion The core of OmniNav fast-slow dual-system architecture: the fast system, condi- tioned on VLM-fused multimodal context, employs flow-matching policy to generate future con- tinuous waypoints, achieving low latency, and high-precision closed-loop control; the slow system plans subgoals and subtasks supported by long-horizon visual memory and frontiers, and introduces explicit CoT for interpretability and self-correction. This architecture supports most basic tasks in embodied navigation. Through unified multimodal tokenization, different tasks (instruct goal, ob- ject goal, point goal) are seamlessly handled within a single model. On the training side, we adopt two-stage scheme, where the second stage's joint training of discrete and continuous values prevents continuous-contro) fine-tuning from eroding the base VLM's capabilities- an approach that can be broadly applicable. We also incorporate sizable ggneral-purpose data and to bolster language understanding, visual semantics, structured reasoning, and commonsense priors for VLN, thereby improving generalization and robustness in embodied navigation. Experimentally. OmniNav improves success rates over the current best on R2R-CE and on RxR-CE, achieves the best performance on OVON, and benefits further from the slow-system design. Real-world quadruped robot deployment demonstrates the engineering feasibility of up to 5 Hz cloud inference with tri- view inputs and a 20-frame history buffer. Overall, the high spatial precision and low latency of continuous waypoints, the unified multimodal interface, the fast-slow system collaboration, and joint training collectively underpin OmniNav's 's strong performance across benchmarks, evidencing solid open-set generalization and practical deployment potential.
data
10

---

REFERENCES
Dong An, Zun Wang, Yangguang Li, Yi Wang. Yicong Hong, Yan Huang, Liang Wang, and Jing Shao. 1st place solutions for rxr-habitat vision-and-languaae navigation competition (evpr 2022).
arXiv preprint arXiv:2206. 11610, 2022.
Dong An, Hanqing Wang, Wenguan Wang. Zun Wang. Yan Huang, Keji He, and Liang Wang. Etp- nav: Evolving topoloiiaal planning for vision-languagge navigation in continuous environments.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, and Anton Van Den Hengel. Vision-and-lan and--and-daaaaa and-language navigation: Interpreting visually-grounde navigation instructions in real environments. In Proceedings of the IEEE con- ference on computer vision and pattern recognition, pp- 3674-3683, 2018
Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, and Motoaki Kawanabe. Scanqa: 3d question an- swering for spatial scene understanding. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 191 19129-19139, 2022.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.1399 139233 2025.
Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan, Yu Fang. Dieter Fox, Fengyuan Hu, Spencer Huang, al. Gr0Ot nl: An open foundation model for generalist humanoid robots. arXiv preprint arXiv:2503. 14734, 2025.
Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Manuel Y Galliker, et al. m0. 5: vision-language- action model with open-world generalization. arXiv preprint arXiv:2504. 16054, 2025.
Qingwen Bu, Jisong Cai, Li Chen, Xiuqi Cui, Yan Ding, Siyuan Feng, Shenyuan Gao, Xindong He, Xuan Hu, Xu Huang, et al. Agibot world colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems. arXiv preprint arXiv:2503.06665 2025.
Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. arXiv preprint arXiv: vrXiv:/709.006 7709.761588 2017.
Matthew Chang, Theophile Gervet, Mukul Khanna, Sriram Yenamandra, Dhruv Shah, So Yeon Min, Kavit Shah, Chris Paxton, Saurabh Gupta, Dhruv Batra, al. Goat: Go to any thing. arXiv preprint MrXiv:2311.064300 2023.
Boyuan Chen, Diego Martí Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitz- mann. Diffusion forcing: Next-token prediction meets full-sequence diffusion. Advances in Neural Information Processing Systems, 37:24081-24125. 2024.
Jiaqi Chen, Bingqian Lin, Xinmin Liu, Lin Ma. Xiaodan Liang, and Kwan- Yee K Wong. Affordances-orienter planning using foundation models for continuous vasion-language naviga- tion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 23568- 23576, 2025a.
Jierun Chen, Fangyun Wei, Jinjing Zhao, Sizhe Song, Bohuai Wu, Zhuoxuan Peng. S-H Gary Chan, and Hongyang Zhang. Revisiting referring expression comprehension evaluation in the of large multimodal models. In Proceedings of the Computer Vision and Pattern Recognition Con- ference, pp. 513-524, 2025b.
Kevin Chen, Junshen K Chen, Jo Chuang. Marynel Vázquez, and Silvio Savarese. Topological planning with transformers for saiion-and-aaguuaee navigation. In Proceedings the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11276-1 1286, 2021.
Peihao Chen, Dongyu Ji, Kunyang Lin, Runhao Zeng, Thomas Li. Mingkui Tan, and Chuang Gan. Weakly supervisd multi multi- granularity map learning for vision-and -nd---nn--naggge navigation. Advances in Neural Information Processing Systems, 35:38 13331166111 2022.

---

Ziyi Chen, Yingnan Guo, Zedong Chu, Minghua Luo, Yanfen Shen, Mingchao Sun, Junjun Hu, Shichao Xie, Kuan Yang, Pei Shi, et al. Socialnav: Training human- human-inspired foundation model for socially embodied navigation. arXiv preprint arXi::511122135,, 2025c.
An-Chieh Cheng, Yandong Ji, Zhaojing Yang, Zaitian Gongye, Xueyan Zou, Jan Kautz, Erdem Bıyık, Hongxu Yin, Sifei Liu, and Xiaolong Wang. Navila: Legged robot vision-laguuage-ctton model for navigation. arXiv preprint M4Xiv:2412.04455. 2024.
Wei-Lin Chiang, Zhuohan Li, Ziqing Lin, Ying Sheng. Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. Vieuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See htlps://vicuna. Imsys. org (accessed 14 April 2023), 2(3):6, 2023.
Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra. Embod- ied question answering. In Proceedings of the IEEE conference on computer vision and patlern recognition, pp. 1-10, 2018.
Vishnu Sashank Dorbala, Gunnar Sigurdsson, Robinson Piramuthu, Jesse Thomason, and Gauray Sukhatme. Clip-nav: Using clip for zero-shot vision-and-language navigation. arXiv preprint arXiv:2211.16649) 2022.
AI Figure. Helix: A vision-language-accin action model for generalist humanoid control. Figure Al News, 2024.
Peng Gao, Peng Wang, Feng Gao, Fei Wang, and Ruyue Yuan. Vision-language navigation with embodied intelligence: A survey, arXiv preprint 240v 2024.
Yuying Ge, Sijie Zhao, Jinguo Zhu, Yixiao Ge, Kun Yi, Lin Song, Chen Li, Xiaohan Ding, and Ying Shan. Seed-x: Multimodal models with unified multi-granularity comprehension and generation. arXiv preprint arXiv:2404. 14396, 2024.
Georgios Georgakis, Karl Schmeckpeper, Karan Wanchoo, Soham Dan, Hleni Miltsakaki, Dan Roth, and Kostas Daniilidis. Cross-modal map learning for vision and language navigation. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 15460-15470, 2022.
Jing Gu, Eliana Stefani, Qi Wu, Jesse Thomason, and Xin Eric Wang. Vision-and-lanuuage nav- igation: A survey of tasks, methods, and future directions. arXiv preprint arXiv:2203.12667, 2022.
Jarvis Guo, Tuney Zheng, Yuelin Bai, Bo Li, Yubo Wang. King Zhu, Yizhi Li, Graham Neubig, Wenhu Chen, and Xiang Yue. Mammoth-vl: Eliciting multimodal reasoning with instruction tuning at scale. arXiv preprint arXiv:2412.05237,, 2024.
Yicong Hong, Zun Wang. Qi Wu, and Stephen Gould. Bridging the gap between learning in dis- crete and continuous environments for vision-and aaddn--nd-lamaa and-language navigation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 15449, 2022.
Junjun Hu, Jintao Chen, Haochen Bai, Minghua Luo, Shichao Xie, Ziyi Chen, Fei Liu, Zedong Chu, Xinda Xue, Botao Ren, et al. Astranav-world: World model for foresight control and consistency. arXiv preprint 0rXiv:2512.2 2025.
Mukul Khanna, Ram Ramrakhya, Gunjan Chhablani, Sriram Yenamandra, Theophile Gervet, Matthew Chang, Zsolt Kira, Devendra Singh Chaplot, Dhruv Batra, and Roozbeh Mottaghi. Goat- bench: A benchmark for multi-modal lifelong navigation. In Proceedings of the IEEE/CVF Con- ference Computer Vision and Pattern Recognition, pp. 16373-16383, 2024.
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: An open-source lision-language-accion model. arXiv preprint arXiv:2406.00924, 2024.
Jacob Krantz and Stefan Lee. Sim-2-sim transfer for vision-and lannguag navigation in continuous environments. In European conference on computer vision, pp. 588 603. Springer, 2022.
12

---

Jacob Krantz, Erik Wijmans, Arjun Majumdar, Dhruv Batra, and Stefan Lee. Beyond the nav- graph: Vision-and lanngagnd- navigation in continuous environments. In European Conference on Computer Vision, pp. 104-120. Springer, 2020.
Jacob Krantz, Aaron Gokaslan, Dhruv Batra, Stefan Lee, and Oleksandr Maksymets. Waypoint models for instruction-guidde guided navigation in continuous environments. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15162-15171, 2021.
Alexander Ku, Peter Anderson, Roma Patel, Eugene le, and Jason Baldridge. Roon-across-romm:
Multilingual lannnnaand navigation with dense spatiotemporal grounding arXiv preprint arXiv:2010.07954, 2020.
Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, and Hao Dong, Manipllm: Embodied multimodal large language model for object- centric robotic manipulation. In Proceedings of the IEEE/CVF Conference on Computer Viston and Puttern Recognition, pp. 18061-18070, 2024.
Bingqian Lin, Yunshuang Nie. Ziming Wei, Jiaqi Chen, Shikui Ma, Jianhua Han, Hang Xu, Xi- aojun Chang, and Xiaodan Liang. Naveot: Boosting Ilm-based vision-and-langee lanaauuggee navigation via learning disentangled reasoning. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2025.
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2210.02747, 2022.
Fei Liu, Shichao Xie, Minghua Luo, Zedong Chu, Junjun Hu, Xiaolong Wu, and Mu Xu, Navfore- see: A unified vision- language world model for hierarchical planning and dual-horizon navigation prediction. arXiv preprint 212002251..01550, 2025a.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, Advances in neural information processing systems, 36:34892 34916, 2023.
Xinhao Liu, Jintong Li, Yicheng Jiang, Niranjan Sujay. Zhicheng Yang, Juexiao Zhang, John Abanes, Jing Zhang, and Chen Feng. Citywalker: Learning embodied urban navigation from web-scale videos. In Proceedingy of the Computer Vision and Patlern Recognition Conference, pp. 6875-6885, 2025b.
Yuxing Long, Wenzhe Cai, Hongcheng Wang, Guanqi Zhan, and Hao Dong. Instructnav: Zero- shot system for generic instruction navigation in unexplored environment. arXiv preprint arXiv:2406.0488 04882, 2024a.
Yuxing Long, Xiaoqi Li, Wenzhe Cai, and Hao Dong Discuss before moving: navigation via multi multi-expert discussions. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 17380-17387. IEEE, 2024b.
Visual language
Yueen Ma, Zixing Song, Yuzheng Zhuang, Jianye Hao, and Irwin King. A survey vision- language-action models for embodied ai. arXiv preprint arXiv:2405. 14093, 2024.
Sharan Nayak, Grace Lim, Federico Rossi, Michael Otte, and Jean-Pierre de la Croix. Multi-robot exploration for the cadre mission. Autonomous Robots, 49, 2025.
David S Olton. Spatial memory. Scientific American, 236(6):82-99, 1977.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the JEEE/CVF international conference on computer vision, pp. 4195-4205, 2023.
Zhangyang Qi, Zhixiong Zhang, Yizhou Yu, Jiaqi Wang, and Hengshuang Zhao. Vln-rl: Vision-
language navigation via reinforcement fine-tuning. arXiv preprint arXiv:2506.17221 2025.
Santhosh K Ramakrishnan, Aaron Gokaslan, Erik Wijmans, Oleksandr Maksymets, Alex Clegg, John Turner, Eric Undersander, Wojeiech Galuba, Andrew Westbury, Angel X Chang, et al. Sabiitt-mmtterrorr 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai. arXiv preprint arXiv: 109, 08.388.. 2021.
13

---

Sonia Raychaudhuri, Saim Wani, Shivansh Patel, Unnat Jain, and Angel X Chang. Language-aligee waypoint (law) supervision for vision-and- lad--auggag navigation continuous environments, arXiv preprint arXiv:2109.15200 2021.
Botao Ren, Junjun Hu, Xinda Xue, Minghua Luo, Jintao Chen, Haochen Bai, Liangliang You, and Mu Xu. Astranav- memory: Contexts compression for long memory. arXiv preprint arXiv:2512.2 1627, 2025.
Koya Sakamoto, Daichi Azuma, Taiki Miyanishi. Shuhei Kurita, and Motoaki Kawanabe. Map- based modular approach for zero-shot embodied question answering. In 2024 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS), pp. 10013-10019. IEEE, 2024.
Ranjan Sapkota, Yang Cao, Konstantinos Roumeliotis, and Manoj Karkee. Vision-Language-actioo models: Concepts, progress, applications and challenges, arXiv preprint arXiv:2505 257,0 04769, 2025.
Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In Proceedings of the JEEE/CVF international conference on computer vision, pp. 4330-849,, 2019.
Haoming Song, Delin Qu, Yuanqi Yao, Qizhi Chen, Qi Lv, Yiwen Tang, Modi Shi, Guanghui Ren, Maoqing Yao, Bin Zhao, et al. Hume: Introducing system-2 thinking in risual-aaguaageaation model. arXiv preprint XXxv:2505.22422 2025.
Hao Tan, Licheng Yu, and Mohit Bansal. Learning to navigate unseen environments: Back transla- tion with environmental dropout. arXiv preprint arXiv: 09041994444999 2019.
BAAT RoboBrain Team, Mingyu Cao, Huajie Tan, Yuheng Ji, Xiansheng Chen, Minglan Lin, Zhiyu Li, Zhou Cao, Pengwei Wang, Enshen Zhou, et al. Robobrain 2.0 technical report. arXiv preprint 02Xiv:2507.02029, 2025.
Zachary Teed, Lahav Lipson, and Jia Deng. Deep patch visual odometry. Advances in Neural Information Processing Systems, 36:39033-39051 2023.
Hanqing Wang, Wei Liang, Lue Van Gool, and Wenguan Wang. Dreamwalker: Mental planning for continuous vision- languan navigation. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 10873-10883, 2023a.
Shaoan Wang, Jiazhao Zhang, Minghan Li, Jiahang Liu, Anqi Li, Kui Wu, Fangwei Zhong, Junzhi Yu, Zhizheng Zhang, and He Wang. Trackvla: Embodied visual tracking in the wild. arXiv preprint arXiv:2505.23188 2025a
Shuo Wang, Yongcai Wang, Wanting Li, Xudong Cai. Yucheng Wang, Maiyue Chen, Kaihui Wang, Zhizhong Su, Deying Li, and Zhaoxin Fan. Aux-think: Exploring reasoning strategies for data- efficient vision-language navigation. arXiv preprint arXiv:2505. 1886, 2025b.
Zihan Wang, Xiangyang Li, Jiahao Yang. Yeqi Liu, and Shuqiang Jiang. Gridmm: Grid memory map for iision-and-languaae navigation. In Proceedings of the IEEF/CVF International conference on computer vision, pp. 15625 -1666, 2023b.
Zihan Wang. Xiangyang Li, Jiahao Yang. Yeqi Liu, Junjie Hu, Ming Jiang, and Shuqiang Jiang. Lookahead exploration with neural radiance representation for continuous dision-languaa navi- gation. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 13753-13762, 2024.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et al. Chain-of-thourt prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24887 2022.
Meng Wei, Chenyang Wan, Xiqian Yu, Tai Wang. Yuqiang Yang. Xiaohan Mao, Chenming Zhu, Wenzhe Cai, Hanqing Wang, Yilun Chen, et al. Streamvin: Streaming vision-and andion--n---agguaee navi- gation via slowfast context modeling. arXiv preprint arXiv:2507.05244 2025.
14

---

Wentao Xiang, Haokang Zhang, Tianhang Yang. Zedong Chu, Ruihang Chu, Shichao Xie, Yujian Yuan, Jian Sun, Zhining Gu, Junjie Wang, et al. Nav-72 dual-relation reasoning for generalizable open-vocabulary object-goal navigation. arXiv preprint arXiv:2512.02400. 2025.
Xiuwei Xu, Huangxing Chen, Linging Zhao, Ziwei Wang, Jie Zhou, and Jiwen Lu. Embodiedsam: Online segment any 3d thing in real time. arXiv preprint arXiv:2408. / 1811, 2024.
Kai Yang, Tianlin Zhang, Zhengho Wang, Zedong Chu, Xiaolong Wu, Yang Cai, and Mu Xu. Ce-nav: Flow-guided reinforcement refinement for cross-embodiment local navigation. arXiv preprint arXiv:2509.23222 23203, 2025.
Naoki Yokoyama, Sehoon Ha, Dhruv Batra, Jiuguang Wang, and Bernadette Bucher. Vifm: Vision- language frontier maps for zero-shot semantic navigation. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 42-48. IEEE, 2024a.
Naoki Yokoyama, Ram Ramrakhya, Abhishek Das, Dhruv Batra, and Sehoon Ha. Hm3d-ovon: A dataset and benchmark for open-vocabulary object goal navigation. In 2024 JEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS), pp. 5543-5550. IEEE, 2024b.
Zhuoyuan Yu, Yuxing Long, Zihan Yang, Chengyan Zeng. Hongwei Fan, Jiyao Zhang, and Hao Dong, Correctnav: Self-correction flywheel empowers vision-language-action navigation model.
arXiv preprint arXiv: 10416, 2025.
Jiazhao Zhang, Kunyu Wang, Shaoan Wang, Minghan Li, Haoran Liu, Songlin Wei, Zhongyuan Wang, Zhizheng Zhang, and He Wang. Uni-navid: A video-based vision-language-aciion model for unifying embodied navigation tasks. arXiv preprint arXiv:2412.06224, 2024a.
Jiazhao Zhang, Kunyu Wang, Rongtao Xu, Gengze Zhou, Yicong Hong, Xiaomeng Fang, Qi Wu, Zhizheng Zhang, and He Wang. Navid: Video-based vlm plans the next step for vision-and- language navigation. arXiv preprint 2XXi:22422.188222 2024b.
Chuyang Zhao, Yuxing Song, Wenhao Wang, Haocheng Feng, Errui Ding, Yifan Sun, Xinyan Xiao, and Jingdong Wang. Monoformer: One transformer for both diffusion and autoregression. arXiv preprint arXiv:2409. 16280, 2024.
Qingqing Zhao, Yao Lu, Moo Jin Kim, Zipeng Fu, Zhuoyang Zhang, Yecheng Wu, Zhaoshuo Li, Qianli Ma. Song Han, Chelsea Finn, et al. Cot-vla: Visual cfain-of-thought reasoning for vision- language-action models. Proceedings of the Computer Vision and Pattern Recognition Confer- ence, pp. 1702-1713, 2025.
Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model. arXiv preprint arXiv:2408) 11440.1.030 1039, 2024a.
Gengze Zhou, Yicong Hong, and Qi Wu. Navgpt: Explicit reasoning in saiion-nnd-anguuaa naviga- tion with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 7641-7649, 2024b.
Hang Zhou, Junqing Yu, and Wei Yang. Dual memory units with uncertainty regulation for weakly supervised video anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 37, pp. 3769-3777, 2023.
Deyao Zhu, Jun Chen, Kilichbek Haydarov, Xiaoqian Shen, Wenxuan Zhang, and Mohamed Elho- seiny. Chatgpt asks, blip-2 answers: Automatic questioning towards enriched visual descriptions. arXiv preprini arXiv:2303.0659. 2023.
Ziyu Zhu, Xilin Wang, Yixuan Li, Zhuofan Zhang, Xiaojian Ma, Yixin Chen, Baoxiong Jia, Wei Liang, Qian Yu, Zhidong Deng, al. Move to understand a 3d scene: Bridging visual grounding and exploration for efficient and versatile embodied navigation. arXiv preprint arXiv:2507.0400 2025.
Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, et al. Rt-2: laabon-lagguag-acttinn models transfer web knowledge to robotic control. In Conference on Robot Learning, pp. 2165-2183, PMLR, 2023.
15

---

A APPENDIX
A.1 ANALYSIS OF TRAINING DATA AND MODEL SCALE
Analysis of Training Data The three additional data components shown in Figure ablated indi- vidually. A qualitative analysis of specifie examples reveals the following: 1) Removing Embodied Q&A or Grounding/referring noticeably degrades performance on small background objects (e.g., "picture", "flowerpot"), suggesting these data improve recognition of small objects. 2) Removing General MLLM data leads to failures on irregular objects (e.g.. "handrail", "stair"), implying gen- eral vision- languaea data helps with such targets. These findings motivate constructing specific datasets to address failure modes. For example, the model still struggles with "carpet" and "clothes" where complex folds and textures are involved; we therefore curate dedicated VQA-style data emphasizing these patterns to strengthen fine-grained visual reasoning on texture-heavy objects.
Table 4: Ablation Study of Data on HM3D-OVON Val-Unseen
Embodied Q&A Data
Grounding and Referring Data General MLLM Data
Ovon-Unseen
55.9
56.5
56.7
57.0
57.7
Analysis of Model Scale The influence of model size (Embodied Q&A, Grounding and Referring, and General MLLM data) is also analyzed. When including these additional data, the 3B and 7B models exhibit nearly identical navigation performance, indicating that once such data are incorpo- rated, simply scaling up the model size brings little further improvement.
In contrast, in the absence of this additional data, noticeable performance gap emerges, with the 7B model outperforming the 3B model. Our analysis suggests two key factors: 1) the performance of the 3B model appears to be constrained by data sufficiency rather than its inherent capacity. When provided with diverse and abundant data, its performance becomes comparable to that of the 7B model, indicating that model size itself is not the primary bottleneck in this data-rich scenario. 2) based on an analysis of failure cases, we find that further performance gains are limited by the intrinsic difficulty of the task: regardless of model size, the recognition of complex objects such as clothes and mirrors remains unstable.
Beyond these two ablation study, a more systematic study of scaling laws and optimal training configurations--e.g how data quality, data composition, and more model size jointly affect per- formance wwold also be highly valuable. Due to computational limits we have not yet conducted such systems-level exploration, and we view this important direction for future work.
Table 5: Ablation Study of Model Size on HM3D-OVON Val- Val-Unseen
Model
Additional Data Ovon-Unseen
3B
55,9
7B
57.2
3B
57.7
7B
57.9
16


Reviews:
[{"review_id": "aN2ofrRkej", "reviewer": "Reviewer_8HoJ", "summary": "This paper introduces OmniNav, a unified framework for embodied navigation that simultaneously supports instruction-guided, object-goal, point-goal, and frontier-based exploration tasks within a single architecture. OmniNav integrates a fast system for low-latency waypoint generation and a slow system for long-horizon planning and subgoal decomposition, coordinated through a central memory module. The model leverages a flow-matching policy for continuous trajectory prediction, and is trained on a diverse mix of navigation and general-purpose vision-language data to improve generalization. Extensive experiments demonstrate state-of-the-art performance across multiple benchmarks (R2R-CE, RxR-CE, HM3D-OVON), and real-world robot deployment validates its practicality and efficiency.", "questions": "See Weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The model jointly support multiple navigation paradigms—instruction, object-goal, point-goal, and frontier-based exploration—within a single model, reducing task-specific customization and promoting cross-task generalization.", "weaknesses": "1. The Dual-System claim is unconvincing and ambiguous.  It is unclear whether the fast and slow systems are two distinct models or a single VLM backbone used in two different modes. If they share the same VLM, the \"dual-system\" description is more of a procedural distinction (e.g., different prompting or output heads). If both systems are built upon a large VLM like Qwen2.5-VL (3B parameters), neither can be considered truly \"fast\" or low-latency in a conventional systems sense. The \"fast\" system's speed seems to be achieved primarily through a shorter context window. And the slow system's contribution is primarily for the Object-Goal navigation (OVON) task with frontier-based exploration. The authors also point out that the fast system alone can actually handle all tasks.  This does not constitute a genuine dual-system design as claimed, and the framing seems to be an overstatement.\n\n 2. The role of coordinates as inputs and outputs is ambiguous across tasks. \nFor object goal, the input coordinates likely represent the locations of frontiers or objects,  the diffusion policy appears to serve only as a low-level controller that generates an executable trajectory towards a pre-selected coordinate.  Given that the system already constructs and maintains an explicit 3D occupancy map for frontier detection, adding the diffusion policy is marginal as using a classical motion planner can also achieve it. Also, the inclusion of a point-goal navigation task within this framework appears redundant. \n\n The authors also fail to provide critical details regarding the planning horizon and physical meaning of the output with 5 spatial-temporal waypoints. The two-stage training paradigm also raises the question: is the flow-matching policy in Stage 2 merely functioning as a \"de-discretization\" module, converting short-term action chunks from Stage 1 into a smoothed but equally short-term trajectory?\n\n 3. The 'Observations' reported in Table 1 are incorrect. The whole system functions by maintaining a 3D occupancy map and identifying frontiers is technically infeasible without depth and odometry information. I’m not sure whether the authors realize that this fundamental reliance on geometric information makes this work a different category from the vision-only baselines.", "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": "i4CcyufUYm", "reviewer": "Reviewer_PyvL", "summary": "This work retrains/finetunes a small general purpose VLM (Qwen2.5-VL-3B-Instruct) for language-guided navigation tasks. The model is jointly trained on object-goal, instruct-goal, and point-goal navigation tasks. The final trajectory output is refined through a 5-step trajectory diffusion head. In their approach the authors use the trained VLM in two modes, calling one \"fast thinking\" and one \"slow thinking\". These roughly correspond to the general established concetps of local and global planning, and in this work are mostly distinguished by which images from a memory bank the VLM gets access to and whether or not Chain-of-Thought-Reasoning is used or not. The system is evaluated on a couple of different simulation benchmarks and validated on a real-world robot deployment.", "questions": "- Figure 3: What is the unit W? \n- Line 294: From my experience, DPVO is producing usually quite bad estimates. There are far more robust monocular SLAM methods (e.g. ORBSLAM, Mast3r-SLAM). Why did the authors choose DPVO and can they quantify the quality of the data that is described in this paragraph?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The overall approach follows a lot of \"conventional wisdom\" in navigation and planning. What is new (at least to me) in this work is the connections the authors make between these established concepts and the operating modes of VLMs: Indeed CoT reasoning fits well to global planning, and changing the information in the prompt is very much like global planning based on maps and local planning based on what is in view.\n- The real-world robot deployment is a very convincing test to me. I am impressed that this system runs at 5Hz on a conventional, not even that new GPU, and it is great to see that it generalizes to what appears to be the office environment of the authors.", "weaknesses": "- The biggest weakness to me in this paper is that there is simply not much to learn. I do not really see any innovation in methodology, this is mostly a system integrating a lot of known components and methodology together. I am all for system papers and I conduct system-level research myself, because it is important to investigate how individual methodology can be integrated together, but system-research really requires much more than just a single ablation study to better understand how to best train a VLM for navigation: E.g. it would be really useful to better understand what kind of training data and training tasks are critical, how much training data is necessary (scaling law), what the range of quality in the training data is, if even larger models would lead to better navigation or if for navigation very large models are not necessary, etc. This paper does not answer any of these questions, it just shows one version of the system and shows that this version works. That is not very insightful.\n- It is quite well hidden and only observable from line 174 that this paper only addresses 2D navigation. That is extremely limiting. I wonder how this fits to the dataset and benchmarks given that HM3D is actually mostly consisting of multi-floor houses that this method is not abel to navigate?\n- One of the claims of the authors is that their system jointly addresses three slightly different ways of how navigation tasks are often framed: Point-goal, object-goal, and instruct-goal. It is not clear to me whether they also evaluate on all of these 3 tasks? The problem here is mostly that for the experiments there are no descriptions of the datasets and benchmarks used, and they are even only named by abbreviation and without reference. What are these benchmarks? What task are they evaluating? With what logic are the baselines selected?\n\n\nThere are further minor inaccuracies and aspects in which the writing and presentation can be improved:\n- Until line 323 it was not clear to me that the fast and slow method actually use the same VLM that is jointly trained. It would help the reader if this is alredy made clear in the introduction of the method, and also in Figure 1.\n- It is not clear how the slow module invokes the fast module once the object is in sight. The caption of Figure 1 claims \"if the target exists in memory, the fast thinking module is invoked\". This contradicts the figure itself, where in the slow thinking moduel there is an option for the target in sight. Also, both modules directly output waypoint coordinates in the figure and there is never a path where the slow module gives input into the fast module.\n- line 196 \"grasp spatial location\". I think this is an overclaim. I do not think it is necessary to understand spatial location in order to produce goals, it is probably sufficient to understand just directions.", "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": "qtpJs3AKZh", "reviewer": "Reviewer_FK7W", "summary": "This paper proposes OmniNav, a unified navigation framework for embodied agents that can perform instruction-goal, object-goal, point-goal and frontier exploration simultaneously. OmniNav generates short-term waypoints with low latency, while leveraging VLM’s chain-of-thought to reason about long-term goals. These two modules are integrated using a central memory module, based on a KV cache. Through experiments the authors show that the system outperforms state-of-the-art baselines.", "questions": "Other than the weakness that I mentioned above, I have a few other questions for the authors.\n1. What happens if another goal type is added? Do you have to train the system from scratch? \n2. Are the 2D coordinates relative to agent starting position or is it a global coordinate?\n3. To evaluate instruction following (RxR), why did you not include the ndtw metric? I believe ndtw is a better representative metric of instruction following than success rate and spl.\n\nMinor comment - missing citation in Line 377", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The overall idea is very interesting and the paper is well-written. Especially, OmniNav separates embodied navigation into a fast and a slow thinking system which is very interesting. Another strength is that the system has been demonstrated to be deployed on a real robot with low latency.", "weaknesses": "Although the architecture in OmniNav is novel, I am not sure if this work is the first unified architecture to support different types of navigation goals, as the authors claim. There have been similar attempts in GOAT[1], GOAT-Bench[2] and InstructNav[3]. Also, many recent works combine frontier exploration and semantic-based frontier exploration[4] within the embodied navigation architecture. I would request the authors to provide more detail about this.", "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": "Zv0ODQq0EV", "reviewer": "Reviewer_ZJRF", "summary": "The paper presents OmniNav, a unified framework that integrates instruction-goal, object-goal, point-goal, and frontier-based exploration within a single embodied navigation architecture. The core design is a fast–slow dual-system: a fast module generates continuous-space waypoints at high frequency using a flow-matching policy on top of a vision–language backbone, while a slow module conducts long-horizon planning with frontier reasoning, visual memory, and explicit chain-of-thought outputs for interpretability. A two-stage training strategy combines discrete autoregressive alignment with continuous control fine-tuning, and further incorporates large-scale generic multimodal data (captioning, grounding, embodied QA). Experiments across R2R-CE, RxR-CE, HM3D-OVON, and CityWalker benchmarks show consistent state-of-the-art results.", "questions": "N/A", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper tackles a highly relevant and challenging goal of unifying multiple embodied navigation paradigms and delivers a coherent architecture that balances long-horizon reasoning with real-time control.\n2. The dual-system design is conceptually grounded and practically validated: the fast module ensures low-latency precision, while the slow module brings reasoning, memory, and semantic exploration, jointly achieving both agility and deliberation.\n3. The training and evaluation are extensive, including multi-task datasets, ablation over all core components, and real-world deployment, demonstrating strong empirical support.", "weaknesses": "1. The framework relies on multiple specialized modules (frontier reasoning, CoT-based subgoal selection, flow matching), yet their interaction lacks a clear learning-based coordination mechanism. The boundary between symbolic reasoning (slow system) and learned policies (fast system) appears manually tuned rather than jointly optimized.\n2. The scalability and computational trade-offs of the dual-system—especially the latency and frequency of the slow module during real-world deployment—are not quantitatively analyzed.", "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)