Embodied Navigation Foundation Model
Authors: Jiazhao Zhang
Anqi Li
Yunpeng Qi
Minghan Li
Jiahang Liu
Shaoan Wang
Haoran Liu
Gengze Zhou
Yuze Wu
Xingxing LI
Yuxin Fan
Wenjun Li
Zhibo Chen
Fei Gao
Qi Wu
Zhizheng Zhang
He Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Embodied Navigation
Vision-Language-Action Model
Primary area: applications to robotics, autonomy, planning
TL;DR: We propose a cross-task and cross-embodiment navigation foundation model.

Abstract: Navigation is a fundamental capability in embodied AI, representing the intelligence required to perceive and interact within physical environments. To achieve such intelligence, recent advanced works leverage Vision-Language Models (VLMs), which demonstrate strong generalizability and possess a well-suited formulation for navigation. However, these approaches remain largely confined to narrow task settings and embodiment-specific architectures. In this work, we introduce a cross-embodiment and cross-task Navigation Foundation Model (NavFoM), trained on eight million navigation samples that encompass quadrupeds, drones, wheeled robots, and vehicles, and spanning diverse tasks such as vision-and-language navigation, object searching, target tracking, and autonomous driving. NavFoM employs a unified architecture that processes multimodal navigation inputs from varying camera configurations and navigation horizons. To accommodate diverse camera setups and temporal horizons, NavFoM incorporates identifier tokens that embed camera view information of embodiments and the temporal context of tasks.  Furthermore, to meet the demands of real-world deployment, NavFoM controls all observation tokens using a dynamically adjusted sampling strategy under a limited token length budget. Extensive evaluations on seven public benchmarks demonstrate that our model achieves state-of-the-art or highly competitive performance across different navigation tasks and embodiments without requiring task-specific fine-tuning. Additional real-world experiments further confirm the strong generalizability and practical applicability of our approach.

Published as a conference paper at ICLR 2026
EMBODIED NAVIGATION FOUNDATION MODEL
Jiazhao Zhang Anqi Li Yunpeng Qi Minghan Li Jiahang Liu
Shaoan Wang' Haoran Liu 1.2 Gengze Zhou" Yuze Wu" Xingxing Li Yuxin Fan
Wenjun Li Zhibo Chen Fei S.7
Qi Wu Zhizheng Zhang He Wang
Peking University Galbot USTC "BAAT
University of Adelaide *Zhejiang University DDifferential Robotics Project Page: https: /tlps://pkue /ttp///kkppppppe wwt.ueepii.gittii github. io/NavFoM-Web. Webbbb/
ABSTRACT
Navigation is fundamental capability in embodied AI, representing the in- telligence required to perceive and interact within physical environments. To achieve such intelligence, recent advanced works leverage Vision-Language Mod- els (VLMs), which demonstrate strong generalizability and possess a well-suited formulation for navigation. However, these approaches remain largely confined to narrow task settings and embodiment--ppecffi architectures. In this work, we in- troduce cross-embodiment and cross-task Navigation Foundation Model (Nav- FoM), trained on eight million navigation samples that encompass quadrupeds, drones, wheeled robots, and vehicles, and spanning diverse tasks such vision- and-language navigation, object searching, target tracking, and autonomous driv- ing. NavFoM employs unified architecture that processes multimodal navigation inputs from varying camera configurations and navigation horizons. To accommo- date diverse camera setups and temporal horizons, NavFoM incorporates identi- fier tokens that embed camera view information of embodiments and the temporal context of tasks. Furthermore, to meet the demands of real-world deployment, NavFoM controls all observation tokens using dynamically adjusted sampling strategy under a limited token length budget. Extensive evaluations on seven public benchmarks demonstrate that model achieves state-of-the-arr highly competitive performance across different navigation tasks and embodiments with- out requiring task-specific fine-tuning, Additional real-world experiments further confirm the strong generalizability and practical applicability of our approach.
INTRODUCTION
For both embodied agents and humans, navigation serves as foundational capability that en- ables them to move intelligently within physical environments accomplish specified tasks (Shah et al., 2023a; Bar et al., 2025; Zhang et al., 2024b). Achieving robust navigation requires deep understanding of environmental context and task instructions, typically presented through visual and linguistic observations, which are reminiscent of Visual Language Models (VLMs). However, VLMs (Liu et al., 2023a; Yang et al., 2024a; Guo et al., 2025) have recently demonstrated remark- able zero-shot generalization in tasks such as retrieval, classification, and captioning from large-scale open-world data, without reliance on nomain-speciiti tion (Savva al., 2019a; Deitke al., 2022) remains tied to narrow task domains, embodiment- specific architectures, and restricted instruction formats.
fine-tuning. In contrast, embodied naviga-
In pursuit of generalist navigation, the community has witnessed growing interest (Zhang et al., 2024a; Cheng et al., 2025; Shah et al., 2023a; Long et al., 2024), yet progress has been hindered by the constrained design and limited domain applicability of prior research. In cross-task naviga- tion, previous methods (Zhang et al., 2025a; Yin et al., 2025; Zhu et al., 2025) typically assume a consistent camera configuration for the robot and unify various tasks such as vision-and- lan-lanuggge navigation, object searching, and target tracking. For cross-embddimen navigation, current ap- proaches (Eftekhar et al., 2024; Hirose al., 2023) implicitly learn priors about the physical shape
Joint First Author Corresponding Author

---

Published as a conference paper at ICLR 2026
Search OVON Val Unsenn
VLN FoR (Single-View)
VLN RXR (Nulti-View)
Tracking EVT BeTcBench
Autonomous Driving NavSin
1C0
64.8
84.5
94.0 13.4
52.4
84.4
56.3
62.0
57.6
50
46.2
40.8
39.5
37.1
362
52.9 49,3
48.7
45.7
48.6
45.3
1677
11.3
Nariial
Usi-herid
DAget
-0b
VLFM
NewFod
Uni-4e
Navid
Hun
ETPN
Reson
Pt
Neeiool
To
EVT gay
Um-Havidd
EV7
LAW
New
PARAA.DMe Dri
UnioD
Diveggory
Figure 1: Benchmark performance of NavFoM, we compare our method with SOTA baselines on cach hench- marks, See Section 3 for more detials.
of the embodiment but are often restricted to specific navigation tasks, The existing divergence be- tween navigation tasks and embodiments highlights the absence of foundational navigation model capable of handling different tasks across diverse embodiments.
In this work, we toward building a cross-task and cross-embodiment embodied navigation founda- tion model, NavFoM, trained on eight million navigation samples spanning diverse embodiments and tasks. Inspired by humans' ability to accomplish a wide range of navigation tasks primarily through visual sensory input and the recent success of vision-only navigation methods (Shah et al., 2023a; Zeng et al.), we formulate the generalist navigation task as processing egocentric videos (captured by one or more cameras mounted on the robot) alongside language instructions, and pre- dicting subsequent trajectories to fulfiil those instructions. This formulation is compatible with most existing navigation task settings (Contributors, 2023; Wang et al., 2024a).
To align generalizable embodiments across diverse camera configurations, we introduce temporal- viewpoint indicator tokens (T'II tokens) to identify both the viewpoint of camera setups and the tem- poral information of the navigation horizon. By dynamically adjusting these TVI tokens, our method enables co-tuning across different camera setups and supports joint training with both image-QA and video-QA samples (Shen et al., 2024; Li et al., 2023). Furthermore, to address the constraints of practical deployment such as hardware memory cost and inference speed, we propose token Budget-Aware Temporal Sampling (BATS) strategy, which dynamically samples navigation history tokens based on forgetting curve constrained by token budget. This token sampling approach balances performance and inference speed, enhaneing the practicality for real-world deployment.
We collected comprehensive and diverse navigation dataset comprising 8.02 million samples, sourced from public navigation datasets (Savva et al., 2019;; Wang al., 2025c; Contributors, 2023; Wang et al., 2024a) and pseudo web-video navigation data (Li et al., 2025a). The dataset includes cross-embodiment trajectories from quadruped robots, drones, wheeled robots, and cars, covering a wide range of tasks such as vision-and- lnd-uuge navigation, object searching, target tracking, and autonomous driving. These navigation samples feature diverse instructions and scenarios that re- quire multiple skills, enabling NavFoM to acquíre generalized navigation capabilities. Additionally, we gathered 4.76 million open-world knowledge samples (Shen et al., 2024; Li et al., 2023) de- rived from both image-based and video-based question-answering tasks. Following the approach of (Zhang et al., 2024a), we co-tune the navigation data together with image and video QA data in an end-to-end manner, facilitating large-scale and comprehensive training of NavFMM.
Our experiments demonstrate that NavFoM achieves substantial advancements in generalist nav- igation. Without task-specific fine-tuning, NavFoM attains state-of-the-ard or competitive perfor- mance across diverse public benchmarks for a variety of embodiments. On VLN-CE RxR (Ku et al., 2020a), NavFoM improves performance multi-camera settings (from 56.3% to 64.4% SR) and in single-camera settings (from 51.8% to 57.4% SR) compared to prior baselines. On HM3D- OVON (Yokoyama et al., 2024b), our method achieves 45.2% SR in a zero-shot setting, outper- forming the previous fine-tuned SOTA method (43.6% SR). Similarly strong results are observed across various benchmarks in object searching, tracking, and autonomous driving. We further val- experiments multiple robotic platforms, including humanoid robots, quadrupeds, drones, and wheeled robots. These results underscore its strong generalizability and highlight promising progress toward generalist navigation.
idate NavFoM through
2

---

Published as a conference paper at ICLR 2026
Trajectory
Plamm Hrait
Large Language Model
Langinge Respoese city
This slanding with diverse landscapes.
VOA
Natspation
Cross-modal Projector
Videocrroge
Grid Average Pooling
Vision Encoders
Aulonomous views)
Image QA
Video QA/ Caption
Robot (1 view) Quadrupted views)
UAV (4 views)
Figure 2: Pipeline of NavFoM. Our method provides a unified framework for handling multiple tasks, includ- ing Image QA, Vidco QA, and Navigation. We organize text tokens and visual tokens using temporal-vicevponn indicator tokens (Sec. 2.1.1).
METHOD
Generalist Navigation Task. We consider a general navigation setting in which a mobile em- bodiment is given textual instruction L and sequence of images 11T on-the-fly fly from N different cameras at time steps [I....T]. Given these observations and the in- struction, model is required to predict navigation trajectory a E R21 = (x, y, 2, 0) represents a position and orientation waypoint. Note that z is only used when the embodiment a UAV, and denotes the yaw angle (since our task does require agile flight motions, the yaw angle suffices). The model drives the mobile embodiment to fulfill the instruction according to the mapping x (L, I!:A) + TT.
captured
{ai, a2,--) where each
Basic Architecture. We extend vanilla video-based vision-language models (VLMs) (Li et al., 2023: Shen al., 2024) dual-branch architecture for both navigation and quustion-answente (Wang et al., 2025c). For navigation, we first encode the observed images 7 1:
1:7 using vision encoders and a cross-modality projector (Liu et al., 2023a) to obtain visual tokens E The instruction is embedded following common practices in existing language models (Liu al., 2023a) to produce language tokens Ez. The visual tokens are then organized via ceapoorl-vieeporr
indicator tokens (sec, 2.1.1) and budget-aware temporal sampling (sec. 2.1.2), concatenated with the language tokens, and fed into large language model to predict the action token. This token is subsequently decoded by a planning model to generate waypoint-based trajectory.
E = LLM( EL),
Tr ActionModel(EE)).
For the question-answerte task, we follow existing methods Liu al. (2023a) and predict the next token in an auto-regressive manner. As existing works (Zhang et al., 2024a; 2025a; Wang et al., 2025c; Cheng al., 2025), our model enables the co-tuning of both navigation and QA samples.
2.1 NAVIGATION FOUNDATION MODEL
Observation Encoding. Given captured egocentric RGB sequences 11:1
R from N
multi-camera views at time step T, we employ pre-trained visual encoders (DINOv2 (Oquab et al., 2023) and SigLIP (Zhai et al., 2023), widely used recipe (Kim et al.; Tong et al., 2024)) to ex- tract visual features V diin/SiglLIP E I8 PxC, where is the number patches (set to 576) and C represents the embedding dimension. For token savings and computational efficiency, we directly concatenate diinoo and Vrsiglip along the channel dimension and denote the resulting representation

---

Published as a conference paper at ICLR 2026
Vi.r. During navigation, on-the-fly captured videos leads extensive number of frames, which subsequently produce an extensive set of visual features. To address this, we employ a grid pooling strategy (Zhang et al., 2024a; 2025a) (Figure 2, Grid Average Pooling) on the visual features to generate more compact representations. Specifically, we utilize two resolution scales:
64 V fine/coaars GridPool(V Or P
(2)
where Vüne R64xC provides fine-grained observations, while V E R offers coarse- grained observations. this case, we use fine-grained features Vine for the latest navigation obser- vation and image QA (at time step 7), while using coarse-grained features for navigation history and video data (across time steps 1: T). Finally, following established VLMs (Liu et al., 2023a; Li cross- cross-modality projector (.)) (a 2-layer MLP) to project visual features into the latent space the Large Language Model: Ey = P(V 1
et al., 2023),
2.1.1 TEMPORAL- VIEWPOINT INDICATOR (TVI) TOKENS.
Given that visual tokens do not inherently incorporate viewpoint and temporal information, key challenge in multi-view navigation models lies enabling the LLM to discern which tokens correspond to different timesteps or distinct camera viewpoints. Previous approaches were limited to either specific camera configurations embod- iments (Long et al., 2024; Gao et al., 2025) or simply concatenated tokens from all viewpoint images (Zheng et al., 2024; Fu et al., 2025b), thereby overlooking the flexibility of LLM token organization. To enable flexible processing of arbitrary camera arrangements, we intro- duce cemporal-viewpoie indicator tokens, inspired by the demonstrated effectiveness of specially designed tokens for time/modality/ask identification (Guo et al., 2025; Chen et al., 2023), approach that has been widely rec- ognized to facilitate LLM learning. In our setting, the in- dicator tokens used in diverse tasks, including image QA, video QA, and navigation, which should meet three important attributes:
2n
125
Angle
3n12
7.5
UMMP
2.5
0.0
Time
28
80
25
5.0
7.5
10.0
12.5
15.0
UMAP
Embowiiing Type
Tanemdagge
Tim
Visualization of Temporal- Viewpoint Indicator (TVT) tokens. We employ a clustering algorithm (McInnes et al., 2018) to map high-dimensional em- beddings into 2D space.
Figure 3:
The token's angle embedding must preserve the circular continuity azimuthal angles (e.g.. = 2T), ensuring that the distance metric between embeddings refleets geometric proximity (e.g., d(0, e) < d(0. T) when * T))
Viewpoint-Awareness:
Time-Awareness: The token must uniquely identify the temporal order of frames across all camera views, while maintaining robustness to irregular sampling intervals.
Separability: The indicator tokens may encode either viewpoint temporal information (for video QA) or may exclude such information entirely (for image QA).
To meet these requirements, our Temporal-Viewpoin Indicator (TVD tokens Eivi E FC (where timestep and view angle denoted and 9, respectively) consist of three types of embeddings: angle embedding AnglePE(()) E IRCC time embedding TimePE(t) E RCC, and a learnable base embedding EBse
C2C:
Egase
PPime (TimePE(t)) + +aa (AnglPPnn)E((()) if Navigation Video QA Image QA
ETVI-
EBase
Piime (TimePH(t)),
(3)
EBssc
where AnglePE(d) is implemented using a concatnation of sinusoidal position encodings (Vaswani et al., 2017) applied to the cosine and sine values of the azimuthal angles separately, and TimePE(t) implemented as a sinusoidal position encoding of t. Here, Pume and Pangle are both implemented two-layer MLPs (similar in design to those used in Liu et al. (2023a)). For different tasks and TVI tokens, we employ different combinations of indicator token components to represent the at- tributes of various visual tokens. For the navigation task, include both temporal and viewpoint
4

---

Published as a conference paper at ICLR 2026
Timestop Video Langth B-1600
PU)) timestop t with fixdd Vidco Longth 7-126
Inferance Time Comparison
B-2048
B-1960
T-125
T=50
0.75
Frome
52
B-1600
2:50
B-1290
200
0.29
0.25
D=960
B=640
292
160
Video Length
50
Emrstte
125
250
350
750
3000
1250
1500
1750 2000
(a) ((t.T) of fixed token budget
P(t 125) with different budget
(c) Inference time of BATS
Figure 4: Visualization of BATS and corresponding time cost. (a) Given fixed token budget B 1600, we illustrate the sampling probability at different timesteps f for the latest timestep T'. (b) Given maximum timestep I' 125, we plot the sampling probability across different timesteps under varying token budgets B. (c) We compare the inference time when using BATS versus not using BATS (keeping all frames).
information. For the video QA task, we incorporate temporal information. For the image QA task, we use only Ease as an indicator that the subsequent tokens are visual tokens. This strategy offers flexible approach organizing significantly different sample types and facilitates LLM learning (Sec. 2.1.3). We provide a plot of the clustering results (McInnes et al., 2018) of TVI Tokens in Figure 3, where observe that the tokens distinguished from one another according to the viewpoint & (represented by a rainbow colorbar) and the timestep t (represented by color value).
2.1.2 BUDGET-AWARE TEMPORAL SAMPLING (BATS).
During navigation, on-the-fly captured video can generate an excessive number of visual tokens, increasing both inference and training time and hindering real-world deployment. Previous methods address this challenge in two ways: (1) Token Merging (Zhang et al., 2025a), which introduces additional computational overhead during training and leads to inconsistent inference speeds during evaluation; (2) Uniform Sampling (Cheng et al., 2025), which often fails to adequately capture recent observations due to lack of short-term context. Moreover, in scenarios involving variable camera-view settings (where the number of frames increases significantly) both strategies require additional modifications.
To this end, we propose Budget-Aware Temporal Sampling (BATS), which is designed for (a) practi- cal purposes (i.e., constraining the maximum token length to accommodate inference speed and GPU memory limitations), (b) retaining more recent information to enhance understanding and planning while preserving sufficient historical context for navigation, and (c) direct adaptability to varying numbers of cameras. Specifically, given a token budget Buken and multi-view vidco sequence II:N E xx3, we employ an exponential growth based sampling probability P(t), which is in- spired by the "forgetting curve". In this case, when the number of captured frame tokens exceeds the token budget, we compute a sampling probability for each frame:
P(() = (1 - ((-T)) +
k>0 0,
where the (we use c = 0.1) ensures that the lower bound of sampling probability is in the approxi- mate range and the k denotes the exponential decay rate. Therefore the expected number of sampled frames can be computed as:
1 e- Brrames S P(t)dt (1-
T+
(5)
k
We constrain the expected number of tokens ((4 1)1Etram + (64 N))N to be no larger than Broken- This implies Etrame Bsenn and with sufficiently large number of frames T, the number of sampled frames will converge to the expectation (Figure 4 (c)). We can offline calculate & for different T using Brent's method (Brent, 2013), leading correspongding P(t) (Equation 4). Note that since we set the lower-bound probability e, Equation may become unsolvable for very large T (e.g., T = 1120 under a four-camera setup with token budget Buken = 2048). However, this
(4+1)N
5

---

Published as a conference paper at ICLR 2026
situation rarely occurs (for the list task in Figure 1), most timesteps are approximately 122 steps in VLN-CE RxR (Ku et al., 2020a). We provide the details of using BATS in Appendix A.2 and a break-in analysis of BATS in Figure 4.
2.1.3 LLM FOWARDING AND TRAINING DETAILS
Token Organization. After obtaining the visual tokens E1-T (sampled via BATS, Sec. 2.1.2) and the language tokens Er, organize these tokens using TVI Tokens (Sec. 1. for forwarding through the LLM. For navigation, we use EBase Pin (TimePE(()) + Pangle (AnglePE()))) to represent both temporal and viewpoint information. Here, fine fine-grained visual tokens used for the most recent observations, while coarse coarse-grained tokens are utilized for historical observations. Our token organization strategy enhances the LLM's 's understanding of the input tokens and supports unified framework for Image QA, Video QA, and navigation tasks. Further details of token orgainzation on Image QA and Video QA can be found in Appendix A.6.
Trajectory prediction. For the navigation task, given the predicted action hidden state E from the forward pass of the LLM, we apply plannning model As (implemented as three-layer MLP) to extract the trajectory information ry- Note that the original trajectory may range from a few meters (indoor navigation) to tens meters (autonomous driving and drones). In this case, directly predict- ing the raw trajectory could lead to divergence in the waypoint distribution. Therefore, following previous methods Shah et (2023a), we normalize the waypoints of trajectories to a distribution of [-1, 1] using task-specific scaling factor Clask- Here, we use three different scaling factors for indoor navigation, UAVs, and cars, as shown in Appendix A.1. We can formulate the trajectory prediction as follows:
{ar. am Jr C'usk Ao(E)))
(6)
where M is set 8, and the normalized trajectory is rescaled absolute values by multiplying by Otask- The trajectory loss is computed using the mean squared error (MSE) Luxv = MSB(7 Tgt there idx denotes the valid action indices. For wheeled robots/car embodiments, ads (x,, y. 0); for UAVs, ads = (z, y, z, f). For the question-answering task, we employ the cross-entropy loss LOA under next-token tee--okkee-rreitionn supervision framework. Given batch containing both navigation and QA samples, the total loss is defined as I. = 3. + T-AA- Here, 3 is a constant scaling factor (set to 10) used amplify the navigation loss, which tends to be numerically small since it is derived from mean squared error. Note that, 3 is important when the training scale is small, where a large can accelerate convergence. We also believe more adaptive way adjust 3 may be a promising direction for future work.
ldx
Training Configurations. Our model is trained on cluster server equipped with 56 NVIDIA H100 GPUs for approximately 72 hours, re- sulting total of 4,032 GPU hours. For guestion-answering data, all frames are sampled at 1 FPS to reduce redundancy between consecutive frames. For discrete navigation data (e.g., Habitat environments Savva et al. (2019a)), we sample each step after the robot performs discrete action (See Appendix A.1 for details on how discrete actions are mod- ified into trajectories.). For continuous navigation environments (e.g., EVT-Bench Wang et al. (2025c), autonomous driving (Caesar et al., 2020b; Contributors, 2023)), data are sampled dundancy. During training, the vision encoders (DINOv2 Oquab et al. (2023) and SigLIP Zhai et al. (2023)) and the large language model (Qwen2-7B Yang et al. (2024a)) are initialized with their default pre- trained weights. Following the training paradigm of VLM (Liu et al., 2023a), we fine-tune only the designated trainable parameters for sin- gle epoch.
number data samples
1.54
Nev Samples 12.7M Samples
LOW-
Numbers
5.9M
FPS avoid
1.2M
NaVid
Uni-NaVid NevFoM Methods
Figure Comprasion of number of training samples with previouse methods.
2.2 DATA
To tne-tune NavFoM, we collect and process a large set of comprehen- sive and diverse training samples, totaling 12.7 million instances. These include 8.02 million navigation samples, 3.15 million image-based question- saatiiion--swering samples, and 1.61 million video-based question-answering samples. The navigation samples are collected

---

To evaluate the performance of NavFoM, we conduct extensive experiments and ablation studies Published as a conference paper at ICLR 2026
and processed from diverse datasets. Specifically, collect Vision-and- Ssiion-andLaanguage Languagge Navigation sam- ples (3.37 M) from R2R (Krantz et al., 2020), RxR (Ku et al., 2020a) and OpenUAV (Wang et al., 2024a); Object Goal Navigation (1.02 M) from HM3D ObjectNav (Savva et al., 2019a); Active Vi- sual Tracking (897 K) from EVT-Bench (Wang et al., 2025c); Autonoums Driving (681 K) from nuScense (Caesar et al., 2020a) OpenScene (Contributors, 2023); and web-video navigation from Sekai dataset (Li et al., 2025a). All navigation data are collected in a unified manner, including previously captured videos (single or multiple cameras), instructions, and predicted trajectory way- points. Further details regarding the navigation samples please refer to Appendix A.4.
Besides navigation data, we, we gather image-based QA (3.15MM M) and video-based QA (1.61 M) data from off-the-shelf datasets following existing video-based Vision-Languee Models (VLMs) (Shen et al., 2024; Li et al., 2023; KunChang Li & Qiao, 2023; Li al., 2024a).
3 EXPERIMENTS
3.1 EXPERIMENT SETUP
addressing three key aspects: (1) How does NavFoM perform on diverse navigation tasks across different benchmarks? (2) How well does NavFoM perform in real-world environments? (3) Are the key design components of our method effective? Our method is compared against strong baselines on each benchmark.
Benchmarks and metrics. We evaluate our method various navigation tasks, including VLN (VLN-CE R2R (Krantz et al., 2020), RxR (Ku et al., 2020a) and Open (Wang et al., 2024a), searching (Yokoyama et al., 2024b), tracking (Wang al., 2025c), and autonomous driving (Caesar et al., 2020a; Dauner et al., 2024b), which are across different embodiments (e.g., egocentric, four- camera, six-camera, and eight-camera configurations). NavFoM uses only online-captured egocen- tric video (some from multi-view sources) and an instruction as input to predict the trajectory for the robot execute. We adopt common evaluation metrics from these benchmarks, including success rate (SR), oracle success rate (OS), success weighted by path length (SPL), normalized Dynamic Time Warping (nDTW), navigation error from goal (NE), and the PDM score Dauner et al. (2024a). For detailed introduction to each benchmark and metric, please refer to Appendix B.
Deployment on synthetic and real-world environemnts. For each navigation task, we adhere to the default settings established in prior works (Krantz et al., 2020; Savva et al., 2019h; Das et al., 2018; Islam al., 2019). For simulators, we use 2048 token gudeget, similar length as base- lines Cheng et al. (2025). Note that for certain benchmarks in Habitat-Lab continuous environments that use discrete actions (such as FORWARD, LEFT, RIGHT, and STOP), we replace these dis- crete actions with trajectory- based actions. For real-world deployment, we employ remote server equipped with an NVIDIA RTX 4090 GPU (use 1600 token budget) to run NavFoM. Under this configuration, the system requires approximately 19.1 GB of GPU memory and achieves infer- ence rate of 5 Hz (about 218 ms per trajectory prediction). Further details on deployment costs are provided in Appendix E.
3.2 BENCHMARK RESULTS
VLN: Performence on VLN-CE (Krantz et al., 2020; Ku et al., 2020a). We begin by evaluat- ing our method on the most widely used vision-and-language instruction benchmarks--L.- R2R and VLN-CE RxR-with the results presented in Table 1. We report performance under both single-camera and four-camera settings (360 observations). Note that our model is not fine-tuned on any specific camera configuration; instead, visual tokens are directly organized using temporal- viewpoint indicator tokens (Figure 10). Our method achieves SOTA performance on both bench- marks across different camera settings. Under the most challenging condition (single-view VLN-CE RxR), our method improves success rate (SR) from 51.8% to 57.4%. Notably, in multi-camera se- tups, our approach uses only four RGB cameras and attains an SR of 64.4%, outperforming previous SOTA methods (56.3% SR) that rely on RGB-D cameras and odometry information. We also ob- serve significant performance gain when transitioning from single-view to multi-view settings: an increase of 5.5% on R2R-CE and 7.0% on RxR-CE, respectively. This suggests that multi-view navigation foundation models represent promising direction for future research. Besides success
7

---

Published as a conference paper at ICLR 2026
Table 1: Comparison VLN-CE in Single-View and Multi-View Settings. Here, S.RGB and M.RGB denote single-view and multi-view configurations, respectively. The symbol indicates methods that utilize the waypoint predictor from (Hong et al., 2022).
Method
Observation
R2R Pal-Unseen
RAR Val-Unseen
S.RGB M.RBB Depth Odo.
NE
OS
SR
SPL
NE. SR
SPL nDTW NDDW
AG-CMIP (Chen et ol, 2021a)
7,90
39.0
23.0
19.0
R2R-CMTP (Chen 2021a)
7,90
38.0
26.0
22,0
HPN+DN (Krantz
CMA" (Hong at. 2022)
6.20
52.0
41.0
36.0
8.76
26.5
22.1
47.0
VENOBERRT (Hoeg
2022)
<
5.74
53.0
44.0
39.0
838
27.0
22.6
46.7
Sim2Sim" (Kranta
2022)
AO Planner (Chen
2024g)
5.58
59.0
47.0
33.0
7.06
43.3
30.5
50.1
GridMM (Wang et 20233)
<
5.11
61.0
49.0
41.0
Egu Map" (Hong
DesamWalker' (Wang al., 2023a)
5.53
59.0
49.0
44.0
Rebom" (An et al, 2022)
<
5.40
57.0
50.0
46.0
1.98
48.6
42.0
63.3
(An 2024)
HNR' (Wang et al., 20246)
4.42
67.0
6L.0
$1.0
5.50
56.3
46.7
63.5
BEYBen" (An et 2023)
4.57
67.0
59.0
50.0
HAMT-ScaleVLNN (Wang 2023c)
NayFoM (Four views)
4.61
72.1
61.7
55.3
4.74
64.4
56.2
65.8
LAW (Raychmadurii
2021)
6.83
44.0
35.0
10.90
8.0
CM2 (Goorgakis et al., 2022)
7,02
41.0
34.0
WS MGMap (Chen et al., 2022)
6.28
47.0
38,0
34,0
Seg2Seq (Krantz al.,
7.77
37.0
25.0
22.0
12.10
13.9
30.8
CMA (Kimntz al., 2020)
7.37
40.0
32.0
30.0
RGB Scu2Sog (Krantz et al, 2020)
10.10
8.0
0.0
0.0
RGB- (Kruniz
2020)
9.55
10.0
5.0
4.0
(Zhang et 2024a)
Uni-NaVid (Zhang at.,, 2025a)
5.58
53,3
47.0
42.7
6.24
48.7
40.9
NaVILA (Cheng al, 2025)
5.22
62.5
54.0
49.0
6.77
49.3
44,0
58.8
Strcam VLN-RGB-all
51.8
NayFoM (Single view)
5.01
64.9
56.2
51.2
5.51
57.4
49.4
60.2
Table 2: Object goal navigation. Comparison HM3D Table 3: Performance EVT-Bench. t: OVON (Yokoyama et al., 2024b). denotes zero-shot eval- Uses GroundingDINO (Liu al., 2023h) uation. report performence of our method on egocentric as open-vocabulary detector. 1: Uses and four-view settings.
SoM (Yang et al., 2023)+GPT-40 (OpenAI, 2024) as the visual foundation model.
VAL.SBEN
VAL SR1
VAL UNSELIN
SRT
SR1
SPL.1
Tarpet TK"
5.4
SK?
DAgger
11.1
544
(Gapto
42,9
56.2
10.6
RL.
18.1
15.0
10.2
4.7
Poliformett (Zeng
2.62
BCRL
39.2
27.8
18.6
EVT (2hong 2024)
39.1
3,23
41.3
29.4
18.3
7.9
EYT] (2hong al. 2004)
49.
15.3
35.7
VLFM (hoeoamm 2024g)
35.2
18.5
32.4
35.2
19.6
Uni-NaVid (Zhang al., 2025aa
25.7
39.5
11.3
274
DA 4DD (Yokeyama sl, 2004ii
18.5
21.1
90.0
21.4
37.1
19.8
TrackV A (Wing 20255)
85.1
786
57.6
643
Uni NaVid" (Zhan et al, 20255)
11.3
21.1
43.9
21.8
39.5
19.8
NavFaM (Singl siew]
85.0
80.5
614
68.2
MTU3D (2h et 2025)
55.0
23.6
25.0
14.7
40.8
12.1
NavFal (Four viems
88.4
31.7
62.9
67.9
Navi oM (Single stew)
37.7
26.1
83.5
20.0
13.6
21.3
Navi oM (Four viems)
40.1
27.1
45.4
33.6
48.2
38.9
rate, also observe that our method achieves higher efficiency, demonstrates higher SPL (56.2% SPL), and produces navigation trajectories that are better aligned with the instructions (65.8 nDTW).
Perfomrence Searching, Tracking and Autonomous Driving.
We conduct experiments to evaluate our method different navigation capabilities, including object goal navigation (Yokoyama et al., 2024b) in Table 2, active visual tracking (Wang al., 2025c) in Table 3, and autonomous driving (Dauner et al., 2024a) in Table 4. We find that our approach demon- strates strong performance compared to strong base- lines that are specifically designed for individual navigation tasks. Moreover, method improves consistently when switching from a single-camera to faur-camera setup, even though it was not trained on the four-camera configuration object naviga- tion and tracking tasks. Additional quantitative
Table 4: NAVSIM navtest split with closed- loop metries.
Method
Camera VL.M- M-seed PDMS
Human
94.8
Constant Velocity Ego Status MLP
21.6
65.6
UniAD (Hu 2023)
83.4
PARA-Drive (Weng et al., 2024) LAW (1. al., 2024h)
$4.0
84.6
DrivingGPT (Chen al., 2024c)
82.4
NavFoM (Eight views)
84.3
8

---

Published as a conference paper at ICLR 2026
Follow the man the whte oottt pur.
MoNe
Track
Go to Houdlisse
Figure 6: Visualization of real-world experiments on cross-task and cross-embodiment settings.
Performance Across Tasks and Data
Performance (1)
02-
50% diverut Spouns, Losk dite
0% Spec dara
100% Data
03.106.
Type
NE 4
RxR Val-Unscon
SPL
37.5
11.7
K2.0
B
B 1024, Uniform Sampling"
Prohability Sampling
5.33
5.28
61.2
59.7
49.6
50,9
57.9
B 1024, Budget-Aware Temporal Sampling
4,98
62.5
53.9
64.1
43-
26-3
26-0
B 2048, Token Merging (7hang al., 2025a)
5.01
63.2
54.9
64.4
B 2048, Uniform Sumplinng*
4,90
62.4
54.0
63.9
20-
B 2048,
Prohability Sampling
4.89
63.0
54.6
B 2048, Budgel--warr Temporal Sumpling
4.74
64.4
56.2
65.8
VUN (54)
Searcting (SR) Taclking (54) Driding (PONG)
Veewpuntthisster postional embedding! Individual Leamned Special Toekns
6.27
52.3
46.3
58.7
Tasks
5.52
59.1
52.0
59.6
Handeraft Toekns (Eiqu. w.a Pagie/ine / Temporal Viewpoint Indicator Tokens (Equ.
6.06
53.6
46.1
58.0
Figure 7: Ablation study on the training of multiple navigation tasks. We report the performance of different training data combinations (specific task data only, spe- cific task data with 50% other data, and specific task data with 100% other data).
4.74
64.4
56.2
65.8
Figure 8: Ablation Study on History Token Organization Strategies and Identity Tokens. Uniform sampling is adopted from (Cheng al., 2025). Poositional embeddings is adopted from HAMT (Chen et al., 2021b).
sults, analyses, and visual examples are provided in Appendix C, Figure 6 and Figure 13. The analysis of both benchmark and real-world experiment failure cases be found in Appendix F.
3.3 ABLATION STUDY
Synergy of training on multiple navigation tasks. We investigate the synergistic effects of multi- navigation task training by comparing the performance of single-task training with co-tuning that incorporates additional data from other navigation tasks (Ku et al., 2020b; Yokoyama et al., 2024b; Wang et al., 2025c; Dauner et al., 2024a). We observe that co-tuning with data from diverse nav- igation tasks leads to consistent performance improvements across all tasks (from 50% to 100% data ratios). Notably, Searching (improving from 10.3% to 45.2%) and Tracking (improving from 12.6% to 62.0%) exhibit the most significant gains. We attribute these improvements to the discrep- ancy between their training conditions (primarily single-view and closed-set target categories) and the evaluation settings, which are multi-view and open-vocabulary. These results suggest that train- ing across multiple navigation tasks helps mitigate overfitting to task-specific navigation patterns.
Effectiveness of BATS and TVI tokens. We conduct ablation studies evaluate the effectiveness of our key designs, including the history token organization strategy and risual-temporg history modeling. The experiments are conducted on the VLN-CE RxR four-camera setting, and the re- sults are presented in Table 8. We test different token strategies under different token budgets (1024 or 2048) and find that BATS outperforms other strategies in both settings, on both token budgets. Specifically, when the token budget is reduced from 2048 to 1024, BATS demonstrates smaller per- formance drop (only 1.4% L) on the nDTW metric compared to the baselines (6.0% + and 5.2% 4). Furthermore, we compare TVI tokens with other common alternatives and find that TVI tokens achieve significantly better performance. As illustrated in Figure 3, we attribute this improvement to the well- rell-learned temporal and viewpoint information. Moreover, compared to the common history- viewpoint positional embedding method (Chen et al., 2021b), we observe a noticeable performance drop. We believe this is due to the additional embedding components introduced for visual tokens, which may increase model complexity, while TVI provides separate information to facilitate LLM understanding, These results demonstrate the effectiveness of TVI tokens.
9

---

Published as a conference paper at ICLR 2026
4 RELATED WORKS
There is a large body of literature (Savva et al., 2019a; Zhang et al., 2024b) on navigation across different tasks and embodiments; here we review those most relevant to our work. In cross-task nav- igation, recent efforts (Wang ct al., 2022; Long et al., 2024; Song et al., 2025; Zhang et al., 2025a; Gao et al., 2025; Yin et al., 2025; Ruan et al., 2025) have shown that integrating data from different categories of navigation tasks can lead to stronger performance across various scenarios. For cross- embodiment navigation, prior studies (Shah al., 2023a;b; Yang al., 2024b; Wang et al., 2020; Eftekhar et al., 2024; Hirose et al., 2023; Putta et al., 2024; Curtis et al., 2024; Wang et al., 2025a; Zhang et al., 2025b; Geng et al., 2025) have demonstrated the potential of transformer-based poli- cies trained on large-scale, cross-embodimg robotic platforms. In this work. our method presents an early attempt to unify cross-task and cross- embodiment navigation within VLA model under unified training and evaluation framework, demonstrating strong performance in both synthetic and real- wolld environments.
datasets to achieve robust performance across various
DISSCUSION AND CONCLUSION
In this work, we propose NavFoM, which aims to push the boundaries of navigation and explore the intelligence learned from coos-emmbddiment and cross-task navigation data. We introduce temporal- viewpoint indicator tokens to enhance the LLM's understanding of varying camera configurations and different horizons in navigation tasks, while also enabling co-training with navigation and data. Furthermore, we employ token budget-aware temporal sampling strategy to balance navigation performance and efficiency, facilitating a unified approach to token sampling across diverse camera setups and task horizons. Extensive experiments on both public benchmarks and real-world environments demonstrate the strong perfomrence and generability of NavFoM. We believe that NavFoM serves as starting point toward navigation foundation model and will attract greater attention to intelligence-centri navigation
ETHICS STATEMENT
This work presents a generalist navigation foundation model designed to enhance the capabilities of embodied agents across diverse environments and embodiments. We acknowledge the potential societal benefits of such technology, including improved assistive robotics, search-and-rescue oper- ations, and autonomous systems. However, we also recognize the risks associated with deploying Al-powered navigation systems in real-worke settings, such safety hazards, privacy concerns aris- ing from visual data collection, and potential misuse. All training data were sourced from publicly available datasets, with due consideration given to ethical guidelines. The development and evalua- tion of our method involved rigorous real-world testing, transparency regarding its capabilities and limitations, and adherence applicable regulations and safety standards.
REPRODUCIBILITY STATEMENT
We provide full implementation details (Section 2), including the model architecture, training con- figurations, data processing procedures, and the real-world deployment framework. All datasets (Section A.4) used are publicly accessible, and hyperparameters are clearly specified in both the main paper and the appendix. The base models (Section 2.1.3), including large language models and vision encoders, are explicitly mentioned in the paper, along with a detailed training strategy. We also include specifics regarding evaluations as well as instructions for deployment in synthetic (Section 3.1) real-world environments (Section B.4). The code, together with pre- tre-trained model weights, will be made publicly available upon acceptance.
ACKNOWLEDGEMEENT
We thank all the reviewers for their constructive comments and suggestions. We also thank Jianmin Wang for his help with the hardware setup. This work is supported by GALBOT.
10

---

Published as a conference paper at ICLR 2026
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-language navigation competition (cvpr 2022). arXiv preprint arXiv:2206. /610, 2022. 8
Dong An, Yuankai Qi. Yangguang Li, Yan Huang, Liang Wang, Tieniu Tan, and Jing Shao. Bevbert: Multimodal map pre pre-training for manuuae--guided navigation. In ICCV, 2023. 8
Dong An, Hanqing Wang, Wenguan Wang, Zun Wang, Yan Huang, Keji He, and Liang Wang. Eip- nav: Evolving topological planning for vision-language navigation in continuous environments.
IEEE TPAMI, 2024.
Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf,, Ian Reid, Stephen Gould, and Anton Van Den Hengel. Vision-and- -lngugge navigation: Interpreting visually-grounded navigation instructions in real environments. In Proceedings of the IEEE con- ference on computer vision and pattern recognition, pp- 3674-3683, 2018. 22
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang. Shijie Wang, Jun Tang, et al. Qwen2. 5-v1 technical report. arXiv preprint arXiv:2502. 2025. 20
Amir Bar, Gaoyue Zhou, Danny Tran, Trevor Darrell, and Yann LeCun. Navigation world models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 15791-15801, 2025.
Dhruv Batra, Aaron Gokaslan, Aniruddha Kembhavi, Oleksandr Maksymets, Roozbeh Mottaghi,
Manolis Savva, Alexander Toshev, and Erik Wijmans. ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects. In arXiv:2006.13171 2020. 22
Donald J. Berndt and James Clifford. Using dynamic time warping to find patterns in time series. In KDD Workshop, 1994. 22
Richard Brent. Algorithms for minimization without derivatives. Courier Corporation, 2013, 5
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1621-11631, 2020a. 7, 20, 21, 22, 26
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11621-11631 2020b. 6, 26
Jiaqi Chen, Bingqian Lin, Xinmin Liu, Xiaodan Liang, and Kwan- K Wong. Affordances- oriented planning using foundation models for continuous vasion-language navigation. arXiv preprint, 2024a.
Jun Chen, Deyao Zhu, Xiaogian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. Minigpt-v2: large language model as unified interface for vision-language multi-task learning. arXiv preprint arXiv:2310.09478, 2023. 4
Kevin Chen, Junshen K Chen, Jo Chuang. Marynel Vázquez, and Silvio Savarese. Topological planning with transformers for assionandd-laguuaa navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11276-1 1286. 2021a.
Peihao Chen, Dongyu Ji, Kunyang Lin, Runhao Zeng, Thomas H Li, Mingkui Tan, and Chuang Gan. Weakly-supervised multi-granularity map learning for vision-and-languag navigation. arXiv preprint 1000::221000006666 2022. 8
Shaoyu Chen, Bo Jiang. Hao Gao, Bencheng Liao, Qing Xu, Qian Zhang, Chang Huang, Wenyu Liu, and Xinggang Wang. Vadv2: End-to-end vectorized autonomous driving via probabilistic planning. arXiv preprint arXiv:2402. 2024b. 25

---

Published as a conference paper at ICLR 2026
Shizhe Chen, Pierre-Louis Guhur, Cordelia Schmid, and Ivan Laptev. History multimodal transformer for vision-and- and-language navigation. Advances Neural Information Processing Sys- tems, 34:5834-5588 5847, 2021b.
Yuntao Chen, Yuqi Wang, and Zhaoxiang Zhang. Drivinggpt: Unifying driving world modeling and planning with multi-modal autoregressive transformers. arXiv preprint arXiv:2412.18607 2024c. 8, 20, 25
An-Chich Cheng, Yandong Ji, Zhaojing Yang. Xueyan Zou, Jan Kautz, Erdem Biyik, Hongxu Yin, Sifei Liu, and Xiaolong Wang. Navila: Legged robot vision-language-action model for naviga- tion. In RSS, 2025. 1, 3, 7, 9, 20
Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, Zehao Yu, Katrin Renz, and Andreas Geiger. Transfuser: Imitation with transformer-base sensor fusion for autonomous driving. IEEE trans- actions pattern analysis and machine intelligence, 45((1)) 2022. 25
OpenScene Contributors. Openscene: The largest up-to-date 3d occupancy prediction benchmark in autonomous driving. https://githut: com cwam///peDDriiii httpp//tw....ceeeeeeeeeeeeeeeeee 6, 7, 20
2023. 2,
Nimrod Curtis, Osher Azulay, and Avishai Sintov. Embodiment-agnostic navigation policy trained with visual demonstrations. arXiv preprint arXiv:2412..00226, 2024. 10
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 pattern recognition, pp. 1-10, 2018. 7
Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng. Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic. Marco Pavone, Andreas Geiger, and Kashyap Chitta. Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking. In
Advances in Neural Information Processing Systems (NeurIPS), 2024a. 7, 8, 9, 26
Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovie, Marco Pavone, al. Navsim: Data-driven non non-reactive autonomous vehicle simulation and benchmarking. Advances in Neural Information
Processing Systems, 37:28706 01728706-88199, 2024b. 7, 21,
Matt Deitke, Eli VanderBilt, Alvaro Herrasti, Luca Weihs, Jordi Salvador, Kiana Ehsani, Winson Han, Eric Kolve, Ali Farhadi, Aniruddha Kembhavi, et al. Procthor: Large-scale embodied ai using procedural generation. arXiv preprint (Bt.:22060069944 2022.
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. Qlora: Efficient finetuning of quantized 1lms. Advances in neural information processing systems, 36: 16:10081-10115, 2023.
28
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. Carla: An open urban driving simulator. In Conference on robot learning, pp. 1-16. PMLR, 2017. 25
Ainaz Eftekhar, Rose Hendrix, Luca Weihs, Jiafei Duan, Ege Caglar, Jordi Salvador, Alvaro Her- rasti, Winson Han, Eli VanderBil, Aniruddha Kembhavi, al. The ring: a robotic indoor navigation generalist. arXiv preprint 2rxiv:24/2.14401 2024. 1, 10
Anthony Francis, Claudia Pérez-d' Arpino, Chengshu Li, Fei Xia, Alexandre Alahi, Rachid Alami, Aniket Bera, Abhijat Biswas, Joydeep Biswas, Rohan Chandra, et al. Principles and guidelines for evaluating social robot navigation algorithms. arXiv preprint arXiv:2306. 16740, 2023. 20
Haoyu Fu, Diankum Zhang, Zongehuang Zhao, Jianfeng Cui, Dingkang Liang, Chong Zhang, Dingyuan Zhang, Hongwei Xie, Bing Wang, and Xiang Bai. Orion: A holistic end-to-end au- tonomous driving framework by vision-language instructed action generation. arXiv preprint arXiv:2503. 19755, 2025a. 26
12

---

Published as a conference paper at ICLR 2026
Haoyu Fu, Diankun Zhang, Zongchuang Zhao, Jianfeng Cui, Dingkang Liang, Chong Zhang, Dingyuan Zhang, Hongwei Xie, Bing Wang. and Xiang Bai. Orion: holistic end-to-end au- tonomous driving framework by vision-language instructed action generation. arXiv preprint arXiv:2503. 19755, 2025b.
Chen Gao, Liankai Jin, Xingyu Peng, Jiazhao Zhang, Yue Deng, Annan Li, He Wang, and Si Liu. Octonav: Towards generalist embodied navigation. arXiv preprint arXiv:2506. 09839, 2025. 4, 10
Haoran Geng, Feishi Wang, Songlin Wei, Yuyang Li, Bangjun Wang, Boshi An, Charlie Tianyue Cheng, Haozhe Lou, Peihao Li, Yen-Jen Wang, et al. Roboverse: Towards a unified plat- form, dataset and benchmark for scalable and generalizable robot learning. arXiv preprint arXiv:2504. 18904, 2025. 10
Georgios Georgakis, Karl Schmeckpeper, Karan Wanchoo, Soham Dan, Eleni Miltsakaki, Dan Roth, and Kostas Danillidis. Cross-modal map learning for vision and language navigation. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15460- 15470, 2022.
Dong Guo, Faming Wu, Feida Zhu, Fuxing Leng, Guang Shi, Haobin Chen, Haoqi Fan, Jian Wang. Jianyu Jiang, Jiawei Wang, al. Seed1. 5-vl technical report. arXiv preprint arXhv:2505.07062, 2025. 1, 4
Meenakshi Gupta, Swagat Kumar, Laxmidhar Behera, and Venkatesh K Subramanian. A novel vision-based tracking algorithm for human-following mobile robot. JEEE Transactions on Sys- tems, Man, and Cybernetics: Systems, 47(7):1415-1427, 2016. 8, 25
Wencheng Han, Dongqian Guo, Cheng-Zhong Xu, and Jianbing Shen. Dme-driver: Integrating human decision logic and 3d scene perception in autonomous driving. In Proceedings of the AAAI Conference Artificial Intelligence, volume 39, pp. 3347-3355, 2025. 26
Noriaki Hirose, Dhruv Shah, Ajay Sridhar, and Sergey Levine. Exaug: Robot-conditioned naviga- tion policies via geometric experience augmentation. 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 4077-4084. IEEE, 2023. 1, 10
Yicong Hong, Zun Wang, Qi Wu, and Stephen Gould. Bridging the gap between learning in dis- crete and continuous environments for vision-and- languuge navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15439- 15449, 2022.
Yicong Hong, Yang Zhou, Ruiyi Zhang, Franck Dernoncourt, Trung Bui, Stephen Gould, and Hao Tan. Learning navigational visual representations with semantic map supervision. In ICCV, 2023.
Shengchao Hu, Li Chen, Penghao Wu, Hongyang Li, Junchi Yan, and Dacheng Tao. St-p3: End-to- end vision-based autonomous driving via spatial-temporal feature learning. In European Confer- ence on Computer Vision, pp. 533-549, Springer, 2022. 26
Yihan Hu, Jiazhi Yang. Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, et al. Planning-oriente autonomous driving. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 17853-17862. 2023. 8, 20, 25, 26
Jyh-Jing Hwang, Runsheng Xu, Hubert I Lin, Wei-Chih Hung Jingwei Ji, Kristy Choi, Di Huang, Tong He, Paul Covington, Benjamin Sapp, al. Emma: End-to-end multimodal model for au- tonomous driving. arXiv preprint arXiv:2410.2300 2024. 26
Gabriel Ilharco, Vihan Jain, Alexander Ku, Eugene Ie, and Jason Baldridge. General eval- uation for instruction conditioned navigation using dynamic time warping. arXiv preprint urXiv:/907.055 2019. 22
Md Jahidul Islam, Jungseok Hong, and Junaed Sattar. Person- Person-following by autonomous robots: A categorical overview. The International Journal of Robotics Research, 38(14):1581- 2019. 7, 20
13

---

Published as a conference paper at ICLR 2026
Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, and Xinggang Wang. Vad: Vectorized scene representation for efficient au- tonomous driving. In Proceedings of the IEEE/CVF International Conference Computer Vi- sion, pp. 8340-8350, 2023. 26
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan P Foster, Pannag R Sanketi, Quan Vuong, et al. Openvla: An open-source
vision-language-action model. In 8th Annual Conference on Robot Learning. 3
Jacob Krantz and Stefan Lee. Sim-2-sim transfer for ssioo-anddlanguaal navigation in continuous environments. In European Conference on Computer Vision, pp. 588-603. Springer, 2022. 8
Jacob Krantz, Erik Wijmans, Arjun Majumdar, Dhruv Batra, and Stefan Lee. Beyond the nav-graph: Vision-and-languagge navigation in continuous environments. In ECCV, 2020. 7, 8, 19, 21
Jacob Krantz, Aaron Gokaslan, Dhruv Batra, Stefan Lee, and Oleksandr Maksymets. Waypoint models for instruction-guudde navigation in continuous environments. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp. 15162-15171, 2021.
Alexander Ku, Peter Anderson, Roma Patel, Eugene Ie, and Jason Baldridge. Noom-across-Tee Multilingual dision-and-language navigation with dense spatiotemporal grounding. In Proceed- ings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 4392 4412, 2020a. 2, 6, 7, 19,21, 26
Alexander Ku, Peter Anderson, Roma Patel, Eugene Ie, and Jason Baldridge. Room-across-room: Multilingual vision-and-languag navigation with dense spatiotemporal grounding. In Proceed- ings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 4392 4412, 2020b. 9
Yi Wang Yizhuo Li Wenhai Wang Ping Luo Yali Wang Limin Wang KunChang Li, Yinan He and Yu Qiao. Videochat: Chat-centric video understanding. arXiv preprint arXiv:2305.06355, 2023.
Yaniv Leviathan, Matan Kalman, and Yossi Matias. Fast inference from transformers via speculative decoding. International Conference on Machine Learning, pp. 19274-19286. PMLR, 2023. 28
Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint 0333:6 2024a. 7
Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. arXiv preprint 1rXiv:2311.17044 2023. 2, 4, 7, 20
Yingyan Li, Lue Fan, Jiawei He, Yuqi Wang. Yuntao Chen, Zhaoxiang Zhang, and Tieniu Tan. Enhancing end-to-end autonomous driving with latent world model. arXiv preprint K4w:2406608481,, 2024b. 8, 20, 25
Zhen Li, Chuanhao Li, Xiaofeng Mao, Shaoheng Lin, Ming Li, Shitian Zhao, Zhaopan Xu, Xinyue Li, Yukang Feng, Jianwen Sun, et al. Sekai: A video dataset towards world exploration. arXiv preprint arXiv:2506. 15675, 2025a. 2, 7, 20
Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, and Noah Snavely. Megasam: Accurate, fast and robust struc- ture and motion from casual dynamic videos. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 14486-10496, 2025b. 20
Zhenxin Li, Kailin Li. Shihao Wang. Shiyi Lan, Zhiding Yu, Yishen Ji, Zhiqi Li, Ziyue Zhu, Jan Kautz, Zuxuan Wu, et al. Hydra-mdp: End-to-end multimodal planning with multi-target hydra- distillation. arXiv preprint arXiv:2406.006 066788 2024c. 25
Bencheng Liao, Shaoyu Chen, Haoran Yin, Bo Jiang, Cheng Wang, Sixu Yan, Xinbang Zhang, Xiangyu Li. Ying Zhang, Qian Zhang, et al. Diffusiondrive: Truncated diffusion model for end- to-end autonomous driving. arXiv preprint arXiv:2411. 2024a. 21, 25
14

---

Published as a conference paper at ICLR 2026
Bencheng Liao, Shaoyu Chen, Haoran Yin, Bo Jiang, Cheng Wang, Sixu Yan, Xinbang Zhang, Xiangyu Li, Ying Zhang, Qian Zhang, et al. Diffusiondrive: Truncated diffusion model for end- to-end autonomous driving. arXiv preprint arXhv:441.15139, 2024b. 20, 26
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In NeurIPS, 2023a. 1, 4,
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang. Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05493 05499, 2023b. 8, 25
Yuxing Long, Wenzhe Cai, Hongcheng Wang, Guanqi Zhan, and Hao Dong. Instructnav: Zero- shot system for generic instruction navigation in unexplored environment. arXiv preprint 04882, arXiv:2406.0m 04882, 2024. 1, 1,4,1 10
Leland McInnes, John Healy, and James Melville. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXin:1802.03326, 2018. 4, 5,
OpenAl. Introducing 4o image generation. https:/ //op/pppaai... comiinde// introducing-44- image, 2024. Accessed: 2025-04-29. 8, 25
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafranice, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07/93, 2023 3,6
Xavier Puig, Eric Undersander, Andrew Szot, Mikael Dallaire Cote, Tsung- Yen Yang. Ruslan Part- sey, Ruta Desai, Alexander William Clegg, Michal Hlavac, So Yeon Min, et al. Habitat 3.0: A co-habitat for humans, avatars and robots. arXiv preprint arXiv:23 13724, 2023. 22
Pranav Putta, Gunjan Aggarwal, Roozbeh Mottaghi, Dhruv Batra, Naoki Yokoyama, Joanne Truong, and Arjun Majumdar. Embodiment randomization for cross embodiment navigation. In 2024 JEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5527-5534. IEEE, 2024. 10
Sonia Raychaudhuri, Saim Wani, Shivansh Patel, Unnat Jain, and Angel Chang. Language- aligned waypoint (law) supervision for vision-and-languuag navigation in continuous environ- ments. 2021.
Shouwei Ruan, Liyuan Wang, Caixin Kang, Qihui Zhu, Songming Liu, Xingxing Wei, and Hang Su. From reactive cognitive: brain-inspired spatial intelligence for embodied agents. arXiv preprint arXiv:2508. htt.:22508.11198... 2025. 10
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra. Habitat: Platform for Embodied AI Research. ICCV, 2019a. 1, 2, 6, 7, 10, 20
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Brik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, et al. Habitat: A platform for embodied ai research. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp- 9339-9347, 2019b.
Dhruv Shah, Ajay Sridhar, Arjun Bhorkar, Noriaki Hirose, and Sergey Levine. Gnm: A general navigation model to drive any robot. In 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 7226-7233. IEEE, 2023a. 1,2, 10
Dhruv Shah, Ajay Sridhar, Nitish Dashora, Kyle Stachowicz, Kevin Black, Noriaki Hirose, and Sergey Levine. ViNT: A foundation model for visual navigation. In 7th Annual Conference on Robot Learning. 2023b. URL https: /aaiiiiiii...//.ii org/ abs /2002/36... 1.6...4444 14846. 10
Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao, Lemeng Wu, Jun Chen, Chenchen Zhu, Zechun Liu, Fanyi Xiao, Balakrishnan Varadarajan, Florian Bordes, al. Longvu: Spatiotemporal adap- tive compression for long video-language understanding. arXiv preprint arXiv.2410.17444. 2024.
2, 3, 7, 20
15

---

Published as a conference paper at ICLR 2026
Xinshuai Song, Weixing Chen, Yang Liu, Weikai Chen, Guanbin Li, and Liang Lin. Towards long- horizon vision- language navigation: Platform, benchmark and method. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 12078-12082 2025. 10
Wenchao Sun, Xuewu Lin, Yining Shi, Chuang Zhang, Haoran Wu, and Sifa Zheng. Sparsedrive: End-to-end autonomous driving via sparse scene representation. arXiv preprint (idi::2005.166200 2024. 26
Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Yang Wang. Zhiyong Zhao, Kun Zhan, Peng Jia, Xianpeng Lang, and Hang Zhao. Drivevlm: The convergence of autonomous driving and large vision-language models. arXiv preprint Arv: 12289, 2024. 26
Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. Eyes wide shut? exploring the visual shortcomings of multimodal llms. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3568-9578, 2024. 3
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention all you need. Advances in neural informa- fion processing systems, 30, 2017.
Haitong Wang, Aaron Hao Tan, Angus Fung, and Goldie Nejat. X-nav: Learning end-to-end cross- embodiment navigation for mobile robots. arXiv preprint arXiv:2507.1473,,
2025a. 2025a.
10
Hanqing Wang, Wei Liang, Luc V Gool, and Wenguan Wang. Towards versatile embodied naviga- tion. Advances in neural information processing systems, 35:36858-36874 2022. 10
Hanqing Wang, Wei Liang, Lue Van Gool, and Wenguan Wang. Dreamwalker: Mental planning for continuous vision-language navigation. In ICCV, 2023a.
Liuyi Wang, Xinyuan Xia, Hui Zhao, Hanqing Wang. Tai Wang, Yilun Chen, Chengju Liu, Qijun Chen, and Jiangmiao Pang. Rethinking the embodied gap in vision-and-language navigation: A holistic study of physical and visual disparities. arXiv preprint arXiv:2507. 13019, 2025b. 19
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 2xXXi:25005.3189 2025c. 2, 7, 8, 9, 20, 21, 24, 25, 26
Shihao Wang, Zhiding Yu, Xiaohui Jiang, Shiyi Lan, Min Shi, Nadine Chang, Jan Kautz, Ying Li, and Jose M Alvarez. Omnidrive: A holistic vision-languas dataset for autonomous driving with counterfactual reasoning. arXiv preprint urXiv:2504.04344 2025d. 26
Xiangyu Wang, Donglin Yang, Ziqin Wang, Hohin Kwan, Jinyu Chen, Wenjun Wu, Hongsheng Li, Yue Liao, and Si Liu. Towards realistic uav vision- language navigation: Platform, benchmark, and methodology. arXiv preprint arXiv:2410.07087, 2024a. 2, 7, 19, 21, 23, 24, 26
Xin Eric Wang, Vihan Jain, Eugene Ie, William Yang Wang. Zornitsa Kozareva, and Sujith Ravi. Environment-agnosse multitask learning for natural language grounded navigation. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIV 16, pp. 413-430. Springer, 2020. 10
Zihan Wang, Xiangyang Li, Jiahao Yang. Yeqi Liu, and Shuqiang Jiang, Gridmm: Grid memory map for lisionaadd-languag navigation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15625-15636, 2023b. 8
Zihan Wang, Xiangyang Li, Jiahao Yang, Yeqi Liu, Junjie Hu, Ming Jiang, and Shuqiang Jiang. Lookahead exploration with neural radiance representation for continuous vision-language navi- gation. In CVPR, 2024b.
Zun Wang, Jialu Li, Yicong Hong. Yi Wang. Qi Wu, Mohit Bansal, Stephen Gould. Hao Tan, and Yu Qiao. Scaling data generation in vision-and lainn-and navigation. In Proceedings of the IEFF/CVF International Conference on Computer Vision, pp. 12009-12020 2023c.
16

---

Published as a conference paper at ICLR 2026
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-lamm and-languaee navi- gation via slowfast context modeling. arXiv preprint arXiv:2507.052400 2025. 8, 20
Xinshuo Weng, Boris Ivanovic, Yan Wang. Yue Wang, and Marco Pavone. Para-drive: Parallelized architecture for real-time autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15449-15458, 2024. 8, 25
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models. In International conference on machine learning, pp. 38087-38099, PMLR, 2023a. 28
Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. Efficient streaming language models with attention sinks. arXiv preprint arXiv: 17453, 2023b. 28
Shen Yan, Tao Zhu, Zirui Wang, Yuan Cao, Mi Zhang, Soham Ghosh, Yonghui Wu, and Jiahui Yu. Videococa: Video-text modeling with zero-shot transfer from contrastive captioners. arXiv preprint wwXv:2212..04979 2022. 22
An Yang, Baosong Yang. Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li. Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang. Jialin Wang, Jian Yang. Jianhong Tu, Jianwei Zhang. Jianxin Ma, Jin Xu, Jingren Zhou, Jinze Bai, Jinzheng He, Junyang Lin, Kai Dang, Keming Lu, Keqin Chen, Kexin Yang, Mei Li, Mingfeng Xue, Na Ni, Pei Zhang, Peng Wang, Ru Peng, Rui Men, Ruize Gao, Runji Lin. Shijie Wang. Shuai Bai, Sinan Tan, Tianhang Zhu, Tianhao Li, Tianyu Liu, Wenbin Ge, Xiaodong Deng, Xiaohuan Zhou, Xingzhang Ren, Xinyu Zhang, Xipin Wei, Xuancheng Ren, Yang Fan, Yang Yao, Yichang Zhang, Yu Wan, Yunfei Chu, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zhihao Fan. Qwen2 technical report. arXiv preprint arXiv:2407. 10671, 2024a. 1,6
Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou, Chunyuan Li, and Jianfeng Gao. Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v. arXiv preprint arXiv:2310.1 1441, 2023. 8, 25
1444,
Jonathan Yang, Catherine Glossop, Arjun Bhorkar, Dhruv Shah, Quan Vuong, Chelsea Finn, Dorsa Sadigh, and Sergey Levine. Pushing the limits of cross-embodiment learning for manipulation and navigation. arXiv preprint arXiv:2402. 19432, 2024b. 10
Hang Yin, Xiuwei Xu, Linqing Zhao, Ziwei Wang, Jie Zhou, and Jiwen Lu. Unigoal: Towards universal zero-shot goal-oriented navigation. In Proceedings of the Computer Vision and Pattern
Recognition Conference, pp. 19057-19066, 2025. 1, 10
Naoki Yokoyama, Schoon Ha, Dhruv Batra, Jiuguang Wang. and Bernadette Bucher. Vlfm:: Vision- language frontier maps for zero-shot semantic navigation. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 42 48. IEEF, 2024a. 8, 24
Naoki Yokoyama, Ram Ramrakhya, Abhishek Das, Dhruv Batra, and Schoon Ha. Hm3d- ovon: A dataset and benchmark for open-vocabulary object goal navigation. arXiv preprint arXiv:2409. /4296, 2024b. 2, 7, 8, 9, 21, 24, 26
Bangguo Yu, Hamidreza Kasaei, and Ming Cao. L3mvn: Leveraging large language models for visual target navigation. In 2023 JEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3554-3560. IEEE, 2023. 19
Chengran Yuan, Zhanqi Zhang, Jiawei Sun, Shuo Sun, Zefan Huang, Christina Dao Wen Lee, Don- gen Li, Yuhang Han, Anthony Wong, Keng Peng Tee, al. Drama: An efficient end-to-end motion planner for autonomous driving with mamba. arXiv preprini arXiv:2408 036001, 2024. 25
Kuo-Hao Zeng, Zichen Zhang, Kiana Ehsani, Rose Hendrix, Jordi Salvador, Alvaro Herrasti, Ross Girshick, Aniruddha Kembhavi, and Luca Weihs. Poliformer: Scaling on-policy rl with trans- formers results in masterful navigators. 8th Annual Conference on Robot Learning. 2, 8, 25
17

---

Published as a conference paper at ICLR 2026
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 11975-11986, 2023. 3, 6
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. Roboties: Science and Systems, 2024a. 1, 2, 4,
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-actioo model for unifying embodied navigation tasks. Robotics: Science and Systems, 2025a. 1, 3, 4, 8, 9, 10, 19, 21, 24, 25,28
Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang, Haoxiang Fu, Xinyu Zheng, Pengwei Wang, Zhongyuan Wang, Wenbo Ding, and Shanghang Zhang. naca3: Understanding any instruction, navigating anywhere, finding anything. arXiv preprint M.Xiv:2508.04598 2025b. 10
Yue Zhang, Ziqiao Ma, Jialu Li, Yanyuan Qiao, Zun Wang, Joyce Chai, Qi Wu, Mohit Bansal, and Parisa Kordjamshidi. Vision-and- lan--uuuee navigation today and tomorrow: A survey in the era of foundation models. ArXiv, 00//2407.07033 2024b. 1,
Duo Zheng, Shijia Huang. Lin Zhao, Yiwu Zhong, and Liwei Wang. Towards learning a generalist model for embodied navigation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 13624-13634, 2024. 4, 19
Fangwei Zhong, Kui Wu, Hai Ci, Churan Wang, and Hao Chen. Empowering embodied visual tracking with visual foundation models and offline rl. In European Conference on Computer Vision, pp. 155. Springer, 2024. 8, 25
Gengze Zhou, Yicong Hong. Zun Wang, Chongyang Zhao, Mohit Bansal, and Qi Wu. Same: Learning generic gunded visual navigation with state-adaptive mixture of experts. arXiv preprint arXiv:2412.05552 2024. 19
Ziyu Zhu, Xilin Wang, Yixuan Li, Zhuofan Zhang, Xiaojian Ma, Yixin Chen, Baoxiong Jia, Wei Liang, Qian Yu, Zhidong Deng, Siyuan Huang, and Qing Li. Move to understand a 3d scene: Bridging visual grounding and exploration for efficient and versatile embodied navigation. Inter- national Conference on Computer Vision (ICCV), 2025. 1, 8, 21, 24
18

---

Published as a conference paper at ICLR 2026
USAGE OF LLM STATEMENT
Large Language Models (LLMs) are utilized solely to enhance the quality of written content by assisting with polishing text and correcting grammatical errors.
A IMPLEMETATION DETIALS
A.1 ACTION PLANNING MODEL.
Due to the fact that different embodiments could have dis- tinct trajectory scales. For instance, indoor robots often move on the scale of meters while cars move on the scale of dozens of meters. We normalize the predicted trajec- tory scaling across different embodiments to the range [- 1.1] of dimesions by multipy a scaling factor C'usk, as reported in Table 5. Note that the scaling factor is not de- rived from the absolute maximum value each dimen- sion; instead, use the 99th percentile of each dimen- sion to avoid the influence outlier data.
Embodiements
x(m)
y(m)
zim)
6(rd))
Indoor robors"
1.0
0.433
2,09
UAV Cars
7.93
3.19
7.85
1.04
50.8
14.9
Table 5: Scaling factors of different dime- siong of predicted tracjtort of different em- bodiements.
A.2 DETIALS OF USING BATS
During navigation, initially when the number of visual tokens is within the token budget B, we retain all visual tokens. Once the visual tokens exceed the budget B, we employ BATS to sample tokens based on a forgetting curve (Sec. 2.1.2). In practice, we precompute P(t, T) for a given token budget to accelerate this process. If the navigation task involves an exceptionally long horizon, such as thousands of steps (which rarely occurs), even using the minimum sampling probability may result the visual tokens exceeding the token budget. such cases, we simply remove the oldest frames.
A.3 DETIALS OF FIGURE 3
We performed clustering (MeInnes et al., 2018) directly on the end-to-end learned TVI tokens (Eq. 3) and visualized the embeddings using a color map based on viewpoint angle and time step. Specif- ically, for the navigation task (Eq. 3 row 1), we sampled 1,800 TVI token embeddings from com- binations 60 angles (distributed over [0, 2xr) and 30 time steps (ranging from 0 to 150). For the VQA task (Eq.3 3 row 2), we sampled embeddings from 30 time steps ranging from 0 to 150.
A.4 DATA PREPARATION
Assion-and-Languag Navigation (3.37 M) requires agent interpret natural language instruc- tions and egocentric visual observations, align the instructions with visual inputs, and plan subse- quent actions reach described landmarks. Following broad definition of VLN (Zheng et al., 2024; Wang et al., 2025b; Zhou et al., 2024), we consider both indoor environments (e.g., VLN-CE on R2R (Krantz al., 2020) and RxR (Ku et al., 2020a)) and outdoor environments (e.g., Open- UAV (Wang et al., 2024a)), deployed on robots and unmanned aerial vehicles (UAVs), respectively. For VLN-CE on R2R and RxR (2.94 M), we capture multi-view RGB videos, annotated instruc- tions, and trajectory data while the robot follows the ground-truth path. The multi-view RGB setup consists of fixed front-view camera and randomly sampled surrounding cameras (ranging from one to eight). Camera heights randomized between 0.6 and 1.5 m, and the horizontal fields of view (HFoV) vary between 75 and 120", For the OpenUAV dataset (429 K), we record camera streams from the front, left, right, and rear views for all sequences. Other randomization strategies remain consistent with those used in the VLN-CE tasks.
Object Goal Navigation (1.02 M) requires robot to explore unseen environment and identify a described target. For the object goal navigation dataset, we follow the method of (Zhang et al., 2025a) by collecting successful episodes from L3MVN (Yu et al., 2023), heuristic-designed ap-
19

---

Published as a conference paper at ICLR 2026
proach that explicitly models the exploration and identification stages. Our data collected from HM3D ObjectNav (Savva et al., 2019a) episodes, which require the agent to locate objects from a predefined category set (e.g,, sofa, chair, and bed). Nevertheless, experiments show that our method generalizes to state-of-the-art open-vocabulary object goal searching, as presented Table 7. Note that we do not employ multiple cameras camera randomization, we aim to maintain the same visual observation configuration as L3MVN.
Active Visual Tracking (897K) (Islam al., 2019; Francis et al., 2023; Wang et al., 2025c) requires the robot to distinguish the target within dynamic and crowded environments. The target is specified via textual instructions. e.g.. "Follow the man in the blue t-shirt." The agent must recognize the appearance of the human, follow the correct person according to the instructions, and maintain an appropriate distance while avoiding obstacles. For this task, we use data from EVT-Bench, consistent with (Wang et al., 2025c), which involves diverse indoor environments and hundreds of avatars with corresponding descriptions. We also incorporate camera randomization, as described in our VLN data collection process.
Autonoums Driving (68IK) (Hu al., 2023; Liao et al., 2024b) requires an agent to generate a safe, confortable, and kinematically feasible trajectory for navigating complex and dynamic real-world environments. This task evaluates the agent's ability to continuously perceive its surroundings, anticipate the future movements of other traffic participants, and make robust sequential decisions to avoid collisions while progressing toward destination. Here, we process 27K and 654K samples sourced from nuScenes (Caesar al., 2020a) and OpenScene (Contributors, 2023), respectively. We directly record the original multi-view images, instructions, and vehicle state information from the dataset. Note that do not collect explicit surrounding information (such as lane details), in contrast to common autonomous driving baselines (Chen et al., 2024c; Li et al., 2024b).
Web-Video Navigation. (2.03M) We also leverage the Sekai dataset (Li et al., 2025a), which pro- vides a collection of approximately 182K YouTube videos with corresponding instructions (gener- ated by VLMs (Bai et al., 2025)) and trajectories (generated by SLAM systems (Li al., 2025b)). Although these navigation samples contain imperfect instructions and trajectories, they remain valuable for incorporating real-world navigation scenarios. Similar findings have been reported in (Cheng et al., 2025; Wei et al., 2025).
Open- World
(4.76M) Following existing video-based VLMs (Li et al., 2023; Shen et al., 2024; Wang et al., 2025c), we collect 3. 3.M5M image QA samples and 1.61M video QA samples, which encompass rich and comprehensive knowledge for open-world understanding.
A.5 DISCRETE ACTION PROCESSING
Front
Right
Rear
Left
Discrete Actions Trajectory VLN-CE
(b) Predicted Trajetocy
Figure 9: Visualization the trajectory (VLN-CE RxR) (a) training (b) evaluation.
For navigation tasks built on the Habitat environment (Savva et al., 2019a), which utilizes low-level discrete actions such as Move Move.Forware Forward, Turn Murn_Rights and Stop. However, the definitions of these discrete actions vary slightly across different navigation tasks. For example, in VLN-CE R2R, Left indicates 15-degree turn, whereas in VLN-CE RxR and HM3D- Nbj, it indicates a 30-degree turn. To unify all navigation tasks with discrete actions, we employ simple strategy: consider moving forward by 12.5 em turning by 15 degrees atomic
20

---

Published as a conference paper at ICLR 2026
.M
LLM
LLM
64
Text
Image
Image
Image
(a) Image QA
(b) Video QA
AAttion Token>
LLM
Image <1,
Image <1,
Image <T, 0:>
Image <T, 0>>
(c) Navigation
Figure 10: Token Organization Strategy of NavFoM Across Different Tasks, (a) For image question an- swering, fine-grained visual tokens utilized, incorporating only the base embedding of TVI tokens. (b) For video question answering, coarse-grained visual tokens are employed, which include both the base embedding and the time embedding TVI tokens. (c) For navigation, both coarse coarse-grained and fine-gra ine-grained visual tokens are used, integrating the base, time, and angle embeddings of TVI tokens.
operation. We then construct the trajectory based the accumulation of these atomic operations. Although the resulting trajectory could be zigzag (Figure 9), after fine-tuning on all navigation datasets, find that the predicted trajectory of our method is smooth and meaningfully directed toward the target.
A.6 TOKEN ORGANIZATION
We provide detailed illustration of the token organization strategy for different tasks in Figure 10. For Image QA, we use Euse along with fine-grained visual tokens (64 tokens per image) to represent the image. For Video QA, we incorporate EBas + PummeeTimme (TimePE(t) to encode temporal information for each frame, and employ hoarse-grained visual tokens (4 tokens per frame) to avoid an excessive number of tokens.
B EXPERIMENT DETIALS
B. BENCHMARKS
We give detailed introduction to evaluation benchmarks:
Vision-and-Langueee Navigation: We evaluate our method on the VAL-Unseen splits of the VLN-CE R2R (Krantz et al., 2020) and RxR (Ku al., 2020a) benchmarks, which require the robot to follow instructions in unseen indoor environments. We also evaluate method on the Open-UAV benchmark (Wang et al., 2024a), which requires the UAV to follow instructions in unseen outdoor environments.
Object goal navigation: We follow previous methods (Zhang et al., 2025a; Zhu et al., 2025) to evaluate the generalizability of object-goal navigation on the HM3D-OVON dataset (Yokoyama et al., 2024b), an open-vocabulary object navigation benchmark, in zero-shot manner.
Active Visual Tracking: We evaluate our method on EVT-Bench (Wang et al., 2025c), a challenging benchamrak requrie the robot distinguish and follow target within crowded environments.
Autonomous Driving: We evaluate our method on mainstream benchmarks, namely nuScenes (Caesar al., 2020a) and NAVSIM (Dauner al., 2024b), for open-loop and evaluation. Our evaluation strategy is consistent with existing baseline (Liao et al., 2024a) ensure fair comparison.
21

---

Published as a conference paper at ICLR 2026
B.2 METRICS
Success Related Metrics. We report three success-related metrics (Anderson et al., 2018): Nav- igation Error (NE) measures the average distance between the agent's final position and the goal; Success Rate (SR) calculates the percentage of episodes where the agent stops within threshold distance the goal, while additionally requiring the goal to be within the agent's 's receptive field for OVON and EVT-Bench; and Oracle Success (OS) reports the percentage of episodes where the agent passes within the threshold distance at any timestep. Success thresholds vary across bench- marks, and we follow their default settings: 0-3m for VLN-CE R2R, RxR, HM3D-OVON; 1-3m for EVT-Bench; and 0 0-20m for Open-UAV.
Trajectory Quality Metries. To account for path efficiency, we measure Success weighted by Path Length (SPL) (Batra et al., 2020), which rewards successful agents that adhere closer to the optimal path length: SPL & Σ> S max((;;,7]] where S; indicates success for episode L3 is the shortest-path distance, and L; is the executed path length; We also report normalized Dynamic Time Warping (nDTW) (Ilharco al., 2019), which quantifies the fidelity of the agent's path relative to the ground truth trajectory: nDTW exp - DTW().)) where (TW(T, +) is the dynamic time warping distance (Berndt & Clifford, 1994) between reference path T and predicted path i, the
and 1) is the shortest-path distance from start goal. Specifically for the tracking task, Tracking Rate (TR) (Puig et al., 2023), which measures the agent's 's temporal consistency, defined as the proportion of steps where the target maintained within the sensor's 's field of view and 1-3m range relative to the total episode length.
Autonomous Driving Evaluations. For the autonomous driving evaluation, we report L2 distance and Collision Rate (CR) for open-loop planning (Caesar et al., 2020a). 1.2 measures the average Euclidean distance between the predicted and ground truth waypoints, while CR measures the fre- quency of intersection with obstacles. For closed-loop evaluation in NAVSIM, we use the PDM score (PDMS) (Dauner al., 2024b). PDMS holistic metric composed of weighted sub-scores: No fa-ft Collisions (NC) and Drivable Area Compliance (DAC) penalize critical safety infrac- tions;
(TTC) and Comfort (Comf.) assess interaction safety and ride smoothness; and Ego Progress (EP) measures the distance traveled along the route as ratio to a safe upper bound.
B.3 TRAINING STRATRGY
Accelerating Training by Caching Visual Features. Due to the long horizon of videos (hundreds of frames), encoding all images on- line in large batch can be computationally expensive. To mitigate this issue, we lever- age visual feature caching mechanism (Yan et al., 2022) and construct a visual feature database (See Figure 11). Note that only cache coarse-grained visual tokens (4 tokens per frame), which require significantly less disk space compared to storing full videos, as sin- gle episode of navigation typically produces dozens of videos. For image QA and the lat- est observation navigation, we still use vi- sual encoders online to extract fine-grained vi- sual tokens (64 tokens per frame). This ap- proach reduces training time (2.9x faster) and GPU memory usage (1.8x less).
Cached Visual Features (Offline)
without cache
2.9x Faster
Vision Encoders (Frozen)
Visual features Database
Avg. Training Time
1.8x Less
MLP Adapter (Trainable)
Visual Tokens
Avg. GPU Memory Cost
Figure 11: Offline Visual Feature Cached. We pre- computed video frames and navigation hisitroy and saved as corase visual tokens.
B.4 REAL-WORLD DEPLOYMENT SYSTEM
We regard our model as a general Visual- Language-Action (VLA) model capable of
22

---

Published as a conference paper at ICLR 2026
Table 6: Comprehensive results OpenUAV benchmark with L1 level assistant. Seen denotes the split, while 0o and UM represent Test Unseen Object Set and Test Unseen Map Set respectively, DA refers to a model trained using backtracking sampling-base based data aggregation. The best and the second best results are denoted by bold and underline.
Method
Test Set
Eiey
Hard
NEL
SRT
OSRT
SPLT
NET
SRT
OSRT
SPLT
NEJ
SRT
OSR?
SPLT
OpenUAVV Seen Set
Random Action
Seen
222.20
0.14
0.21
0.07
142.07
0.26
0.39
0.13
320.12
0.00
0.00
0.00
Fixed Action
Seen
188.61
2.27
8.16
1.40
121.36
3.18
11:48
2.14
270.69
0.79
-1.09
0.49
CMA (Wang 2024g)
Seen
135.73
8.37
18.72
7.90
84.89
11.48
24.52
10.68
197.77
4.57
11:65
4.51
TravelUAV (Wang al.,
106.28
TraveUAVVDA
Seen
98.66
17.45
48.87
15.76
66.10
20.26
51.23
18.10
138.04
14.02
45.98
12,90
NavFoM (Four views)
Seen
93.05
29.17
49.24
25.03
$8.98
32,91
$3.16
27.87
143.83
23.58
43.40
20.90
OpentAVV Unseen Set
0.48
302.96
Pixed Action
U,0
212.84
3,66
9.54
2.16
151 /66
6.70)
13.88
3.72
243.29 233.29
2.14
7.38
1.38
CMA (Wang et al., 2024g)
UO
155.79
9.06
16.06
9.68
102: 92
14.83
22.49
13.90
182.09
6.19
12.
6.08
TravelUAV (Wang al., 2024a)
UO
118.04
22.42
46.90
20.51
$6.12
24.40
49.28
22.03
134.03
21.43
45.71
19.75
NavFoM (Four views)
47.99
27.20
70.51
32.54
28.03
Random Action
UM
202 9M
0.00
0.00
0.00
158.46
0.00
0.90
0.00
26-5 8X
0.00
0.00
0.90
Fixed Action
UM
180.47
0.52
2,61
0.39
132.89
0.89
4.28
0.67
247.72
0.00
0.25
0.00
(Wang 2024g)
TravelUAy (Wang at., 20244)
138.80
4.18
20.77
102.94
4.63
189 46
17.88
3.28
NarFoM (Four views)
UM
125.10
6.30
18.95
5.88
102.4
6.77
20.01
6.04
170.58
5.36
15.71
4.97
driving different embodiments to complete var-
ious navigation tasks. To achieve this, our model takes visual observations -obrainnnns-ooiieeeed from one or more cameras-alont along with instructions, and directly predicts a trajectory. We then utilize ohe- shelf APIs (which may include Lidar other sensors if necessary) specific to each embodiment to drive the robot along the predicted trajectory.
oobtained
Sever
Navigation Foundation Model
(GeForce RTX 5090)
(t))
Compressed upload
(t)) Trajectory
Unitree Go2 SG35 camera
Galbot G1 SG35 camera
Controller
Individual Robot s Local Planner
(Mutli-view)
Velocity/Joints
RGB images
Embodiments
Robots
NCS-B Aerial Robot Realsense D435
Unitree G1
Realsense D455
(a) Real-world deployment system
(b) Robot Setup
Figure 12: Real-world deployment setup. We provide the system architecture of our methods and the corre- sponding robots that were tested in the paper.
An illustration of our real-world system is provided in Figure 12. Specifically, we deploy our model on a remote server equipped with a GeForce RTX 5090 GPU and use the Internet for communication between the server and the client (which includes the controller and embodiments). Given user instruction, the robots compress their current observations and transmit them to the server. The server then processes both the observations and the instruction output trajectory. This trajectory subsequently processed by the local planner of each individual robot, which sends appropriate commands (e.g., velocity or joint controls) to drive the robot.
23

---

Published as a conference paper at ICLR 2026
Table 7: Object goal navigation. Comparison HM3D-OVON Yokoyama et al. (2024b). denotes shot evaluation. We report the performence of our method egocentric four-view settings. best and the second best results are denoted by bold and underline.
VAL SEEN SYNONYMS
SEEN
VAL UNSEEN
Method
SR1
SPL.
SR1
SPL.
SR1
SPL
BC
11.1
4.5
9.9
3.8
5.4
1.9
DAgger
11.1
9.9
5.4
1.9
RI.
18.1
10.2
4.7
BCRL
39.2
27.8
11.7
18.6
7.5
DAgRL
41.3
21.2
29.4
14.4
18.3
7.9
VIFM (Yokoyama et al., 2024a)
35.2
18.6
32.4
17.3
35.2
19.6
DAgRL+OD (Yokoyama ct al., 2024b)
38.5
21.1
39.0
21.4
37.1
19.8
Uni-NaVid" (Zhang et al., 2025a)
41.3
21.1
43.9
21.8
39.5
19.8
MTU3D (Zhu et al., 2025)
55.0
45.0
14.7
40.8
12.1
NavFoM (Single view)
37.7
43.3
29.9
43.6
31.3
NavFoM (Four views)
40.1
27.1
45.4
32.6
45.2
31,9
C ADDITIONAL EXPERIMENTS
C.I PERFORMANCE ON OPENUAV
We report the performance of our method challenging UAV scenario (Wang et al., 2024a) in Table 6, which requires the UAV follow natural language instructions and execute Long-horizon trajectories (averaging 200 meters) to reach described targets in outdoor environments. Note that method uses trajectories directly collected from the TravelUAV (Wang et al., 2024a) training split (mimicking ground truth trajectories), as no strong baseline was available to collect expert trajectories
done for the ObjectNav data collection. Despite this, approach achieves state- of-the-art performance compared to prior UAV-specific baselines such as TravelUAV, without relying on downward- facina cameras as used those methods (we plan to incorporate additional degrees of freedom in camera configurations in future work). This clearly demonstrates the effectiveness of our approach and the benefits of learning from diverse navigation tasks (Figure 7).
However, we observe that all methods perform poorly on the Unseen-Map split, which requires an average traversal of 300 meters through complex neighborhoods to reach unseen targets. This be- cause the unseen split demands more advanced navigation capabilities, such as efficient exploration of scale environments, which turn relies on higher-quality UAV data.
C.2 PERFORMENCE ON OVON
Following prior work (Zhang et al., 2025a; Zhu al., 2025), we evaluate search capability on an open-vocabulary benchmark (Yokoyama et al., 2024b) under zero-shot setting. The results are presented in Table which includes performance for both single-camen and four-camera config- urations. Under the single-camera setting, our method achieves performance comparable to that of state-of-the-art (SOTA) approach (Zhu al., 2025) on both the VAL SEEN and VAL SEEN SYNONYMS splits in zero-shot evaluation setting. On the more challenging VAL UNSEEN split, our method outperforms the SOTA method, improving the success rate (SR) from 40.8% to 43.6%. Furthermore, when transitioning from the single-camera to the four-camera setting, ob- serve consistent improvements across all splits and metrics. Notably, our model was trained only on single-camera search samples, demonstrating that co-tuning across different camera configurations enhances generalization to varied camera setups.
C.3 PERFORMANCE ON THE EVT-BENCII
We evaluate our method on EVT-Bench (Wang et al., 2025c) (including both the Single Target and Distracted Target splits) under both single-view and four-view camera settings (Table 8). Note that our model is trained only on the single-view setting and evaluated on the four-view setting in a zero- shot manner. Our results demonstrate that the proposed method achieves of-t-of-the-ane (SOTA) performance under the single-view setting, outperforming the previous baseline, Track VLA (Wang al., 2025c), which was specifically fine-tuned on tracking data. Furthermore, when the camera
24

---

Published as a conference paper at ICLR 2026
Table 8: Performance EVT-Bench. Uses GroundingDINO (Liu et al., 2023b) the voca-vocy detector. Uses SoM (Yang al., 2023)+GPT-40 (OpenAI, 2024) as the visual foundation model. The best and the second best results are denoted by bold and underline.
Single Target TR1
Distracted Target
Method
SR
SRT
TR
IBVS (Gupla al,, 2016)
42.9
56.2
10.6
28.4
PoliFormerf (Zeng et al.)
4.67
15.5
2.62
13.2
EVT (Zhong et al., 2024)
24.4
39.1
3.23
11.2
EVT; (Zhong et al., 2024)
32.5
49.9
15.7
35.7
Uni-NaVid (Zhang et al., 2025a)
25.7
39.5
27.4
TrackVT.A (Wang
2025c)
85.1
78.6
57.6
63.2
NavFoM (Single view)
85.0
80.5
61.4
68.2
NavFoM (Four views)
88.4
80.7
62.0
67.9
Table 9: Comparison planning-oriented NAVSIM navtest split with closed-loop metrics. V8192 denotes 8192 anchors. The best and the second best results denoted by bold and underline.
Observation & Structure
Metrics
Method
Camera
Lidar
VLM Based
NC11
DAC 1
TTC 1
Comf.
EP 1
PDMS
Human
100
100
100
99.9
87.5
94.8
Constant Velocity
69.9
58.8
49.3
100
49.3
21.6
Ego Status MLP
93.0
77.3
83.6
100
62.8
65.6
(Chitta et al., 2022)
97.4
92.8
79.0
Transfuser (Chitta et al., 2022)
97.7
92.8
92.8
100
79.2
84.0
VADv2-Vessn Vason (Chen et al. 2024b)
97.2
89.1
91.6
100
76.0
80.9
Hydra-MDP-Vauuz (Li et 2024c)
91.7
100
77.6
83.0
DiflusionDrive (Liso et al. 2024a)
98.2
96.2
94.7
100
82.2
88.1
DRAMA
al., 2024)
/
UniAD (Hu et al., 2023)
97.8
91.9
92.9
100
78.8
83.4
PARA-Drie (Weng al., 2024)
97.9
92.4
93.0
99.8
79.3
84.0
LAW et al., 2024b)
96.4
95.4
99.9
81.7
84.6
DrivingGPT (Chen et 2024c)
98.9
90.7
94,9
95.6
79.7
82.4
NavFoM (Eight
setup is increased from single-view to four-view (in a zero-shot manner), our method continues to improve its performance. However, compared to the improvement observed in VLN (a 6.8% 1 in SR on VLN-CE RxR), the gains here are relatively modest (0.6% 1 SR). We attribute this to the fact that most targets in EVT-Bench benchmark. We plan to further investigate this issue through both simulation and methodological enhancements, such incorporating randomly positioned surrounding targets in future work.
spawned in front of the robot, key assumption of this
C.4 PERFOMRENCE ON NAVSIM
We conduct experiments to evaluate our method on eight-view settings autonoums driving (without fine-tuning for specific configurations). Results on NAVSIM Table We observe that our method achieves performance comparable to SOTA methods on both benchmarks, without explicitly model- ing driving-related information such as lane markings, nearby vehicles, or other contextual elements. descriptions prompts,
We believe our approach can be further improved by incorporating similar to other baseline methods. We are also interested in evaluating this model in closed-loop autonomous driving simulators such (Dosovitskiy et al., 2017).
25

---

Published as a conference paper at ICLR 2026
Table 11: Computational cost. We report the converged mem cost and converged inference speed.
Model Version
Converged Mem Cost Converged Inference
RTX 4090 original (16bit)
19.8 GB
218 ms
RTX 4090 Quantized (4bit)
10.7 GB
248 ms
Jetson Thor Quantized (16bit)
19.1 GB
566 ms
C.5 PERFOMRENCE ON NUSCENE
Table 10: Comparison planning-oriented nuScenes dataset with open-loop metries. Metric calcula- tion follows DiffusionDrive (Liao al., 2024b). The best the second best results are denoted by bold and underline.
Observation & Structure
L2 (m) |
Collision (%) J
Method
Camera
VLM-Based
1s
2s
38
Avg.
1s
2s
3s
Avg.
ST--3 (Hu et al. 2022)
1.33
2.11
2.90
2.11
0.23
0.62
1.27
0.71
UniAD (Hu et al., 2023)
0.45
0.70
1.04
0.62
0.58
0.63
0.61
VAD (Jiang al., 2023)
0.41
0.70
1.05
0.72
0.07
0.17
0.41
0.22
SparseDrivve (Sun er al., 2024)
0.29
0.58
0.96
0.61
0.01
0.05
0.18
0.08
DiffusionDrrive (aiao et al., 2024h)
0.27
0.54
0.90
0.57
0.03
0.05
0.16
0.08
DriveVLM (Tian al., 2024)
0.18
0.34
0.68
0.40
0.10
0.22
0.45
0.27
EMMA (Hwang al., 2024)
0.14
0.29
0.54
DMF-Driver (Han et 2025)
0.45
0.91
1.58
0.98
0.05
0.28
0.55
0.29
Omni-Q (Wang el al., 2025d)
0.14
0.29
0.55
0.33
0.00
0.13
0.78
0.30
Omni-L (Wang et ul., 2025d)
0.15
0.36
0.70
0.40
0.06
0.27
0.72
0.35
ORION (Fu et al., 2025a)
0.17
0.31
0.55
0,34
0.05
0.80
0.37
NavFoM (Six views)
0.26
0.39
0.60
0.07
0.18
0.12
We report the performance of our method on six-camera setting autonomous driving benchmark nuScene (Caesar al., 2020b) Table 10. We compare our method with strong baselines that are specifically designed for autonomous driving. Nevertheless, our method achieves comparable performance these methods without explicitly modeling driving-related information.
C.6 VISUAL RESULTS OF SYNTHETIC ENVIRONMENTS
We provide visual results benchmarks in Figure 13 from VLN-CE RxR (Ku et al., 2020a), EVT- Bench (Wang et al., 2025c), OVON (Yokoyama et al., 2024b), openUAV (Wang et al., 2024a), nuScenes (Caesar al., 2020a) and NAVSIM (Dauner al., 2024a).
D ABLATION STUDY
Performance on differnt number of cameras. We evaluate the effectiveness of incorporating additional cameras in navigation tasks VLN-CE RxR, benchmark that offers relatively com- prehensive suite of vision- lisionelanguage navigation challenges. The results are presented in Table 14, which compares configurations of one, two, three, four, and six cameras mounted around the robot to achieve a wider field of view. We observe consistent performance improvements when increas- ing the number of cameras from one to four, validating that enhanced environmental observations contribute positively to navigation performance. Notably, however, expanding to six cameras leads to slight degradation performance. We attribute this to the fact that six cameras do not provide substantially more observational coverage compared to four cameras, while the increased number of view tokens reduces the capacity available for encoding historical frames (Equation 5). This weaks the alignment between the navigation history and the instruction. We suggest that this issue could be mitigated by adopting an adaptive multi-view token encoding strategy. To maintain coherence in the current work, we leave this exploration for future research.
26

---

Published as a conference paper at ICLR 2026
senee whee wue hang wan di pue do kd wew Whe befoom dn an bum tah THis duaeet poe Hawio
out
nou villeer we
VUN CE RAR
Step
Step 20
L Step
Follow the person dressed in light gray shirt and dark pants.
119
150
EVT CVT-Bench DT
Seurch for a/an microwave, and stop by the microwave
HM3D-OVON
A yellow posilionee deserted strect characterized low-rise brick and concrete buildings, sidewal lks, there's a phone nearhy. Please control drone target
Step
Step
Step 28
Step S2
Step 60
Step 70
OpenUav
Construction zone, stopped red light, parked cars...... (vehicle state information)
Step
Step
Step 32
nuScenes
ohone
Tum Left
(vehicle state information)
NAVSIM
Figure 13: Visualization of perfomrence benchmarks. We report visual results of NavFoM VLN-CE RxR (single-vicw), EVT-Bench Distracted Targets (four-view), OpenUAV (four-view), NeuScenes (six-vior sixx-veew,, OpenScenses (Eight-view).
E REAL-WORLD EXPERIMENTS
Real-world deployment cost. We have conducted additional experiments on deployment costs. Specifically, we provide the original costs (16-bit) and quantized version (4-bit) of our model ('7B LLM + 2B ViT, 2048 Token Budget, four-camera view") on VLN-CE RxR in the table. The results can be found in Table 11. We find that our quantized models (4-bit via Bitsandbytes' significantly
27

---

Published as a conference paper at ICLR 2026
Effect of Camera Count VLN-CE RxR
70
65.8
65.4
65
Performance (%)
62.0
61.1
58.7
62.3
63.1
58.4
56.2
55,4
53.6
Metrics
50.5
50
SR
SPL
45
nDTW
Figure 14: Ablation study the number of cameras in VLN-CE RxR. We report the performance under five different camera configurations (from left right: one-, two-, three-, four-, and six-camera settings), with same token budget (B 2048).
reduce deployment costs (using only 53.9% of the original memory) while maintaining comparable performance. This also enables our method be deployed on the latest onboard GPUs. As an example, we deployed the 16-bit quantized model on a Jetson Thor and observed stable performance, with an average inference speed of 566 ms per trajectory prediction.
Regarding deployment memory cost, techniques such SmoothQuant (Xiao et al., 2023a) and quantization-awart training (Dettmers et al., 2023) could significantly reduce memory usage while maintaining strong performance. For inference speed, there also existing advanced techniques such as LLM streaming (Xiao et al., 2023b) (which suitable for processing online captured video in robot tasks) and the Speculative Decoding strategy Leviathan al. (2023). These methods have demonstrated significant inference speed improvements in complicated tasks (Leviathan et al., 2023). In summary, believe that with the rapid development of graphics hardware and accel- eration methods, fast and convenient deployment of large model-based approaches will become a promising direction.
Real-world performence on 110 reproducible test cases. To evaluate the real-world performance of method, we designed a series of navigation cases with different capabilities (including 50 VLN samples, 30 search samples, and 30 tracking samples). Specifically, we constructed 5m 5m space and recorded the locations of the robot, obstacles, and targets for each test case. We report both qualitative and quantitative results of NavFoM in complex scenarios across these navigation capabilities. The results are presented in Figure 15. Our findings indicate that NavFoM demonstrates strong real-world performance: it correctly understands the surrounding environment and plans appropriate trajectories to accomplish the task. Moreover, compared the strong baseline Vni (Zhang et al., 2025a), our method exhibits significant improvements across both tasks, demonstrating its superior performance in real-world environments.
Visual results of challenging cross-task and cross-emdbodiement real-world experiments. We also conduct extensive experiments on more challenging scenarios with different embodiments (quadruped robots, humanoids, drones, and wheeled robots). The results shown in Figure 16, where we find that our method can handle complicated real-world environments and fulfill long- horizon instructions. We encourage readers view our accompanying videos for more intuitive demonstration.
F FAILURE CASE ANALYSIS
We provide a more detailed analysis of the failure cases, covering both benchmark and real-world environments.
Benchmark Environments: We analyze benchmark failure cases in in VLN-CE RxR, the limited field of view (FoV) in the single-camera setup significantly affects the ability ground visual in-
28

---

Published as a conference paper at ICLR 2026
Now Tocker temi now
Moue tothe chair bebind the box
Fino and move tortin barkkock
Frvdl and Preeete to the siswieelage
Trekk the Robot Deg
Fo ow the man
Follawe the too
Mwxi fowel
Nowe uumanadd nobot, jum bas
the sacend chai and stop
black chai
Lan nit baturad 10% fur tan rour
bottle
aood by both
Swon dol and
lit
Real-world Experiment Perfomence
arch for the bin
83
110
56
reproducible test
Success
VLN
Searching
Tracking
Figure 15: Real-world experiments. We report both the qualitive and quantitive results of NavFoM plex seniors among different navigation cabability.
formation with instructions. When switching to a four-camera setting (3602 FoV), the success rate increases from 57.4% to 64.4%. We observe that about 51% of failures are due dataset/simula- tor problems, including rendering quality and misleading instructions (e.g.. ambiguous landmarks). The remaining failures stem from model capability issues (49%), such as failing to align history with instructions (e.g., performing early stops) failing to execute sufficient turns (especially chal- lenging narrow corners). This indicates that future efforts should focus on improving both dataset- s/simulators and model capabilities.
During the real-world experiments, we find that most failure cases stem from recognizing small objects (such bottles books) from long distance understand- ing blurred images while the robot is moving. Additionally, extremely challenging scenarios, such following long-horizon instructions (thousands of words) searching large building (hundreds of square meters), pose critical challenges to the method. We believe that a more robust real-world approach requires collaborative efforts both model capabilities (percep- tion, reasoning, memory) and hardware components (camera, computational resources).
Real-world Environments:
object within very
29

---

Published as a conference paper at ICLR 2026
disk Contrue hellway along hullsary, presing Finzlly, appenach stop
Egocentric View Third Third-Perssin Person View
proceed glass
Third Person View
mon white top.
Egocentric View
Track the in white T-shirt
Move the black chair, then the black box,, and then move to the white cylinder
Thire- Phire-Person View
Egocentric View
Turn Left and move inlo the door and stop the humanoid robot, then continue forward and by the TV
Third Person View
Egocentric Veww
La
Switch from the four-comera Unitree Go2 single single-comera Unitree Follow the man the white T-shirt and jeans.
Ggocentric View Third Third-Person Person View
Figure 16: Visualization of real-wworl solldwrldd experiments cross tross-task and cross eobodimmbo settings.
30


Reviews:
[{"review_id": "oJEJsQJcCy", "reviewer": "Reviewer_E4q1", "summary": "The primary contribution of this research is the design and training of a unified navigation foundation model (NavFoM). This model was trained on a dataset comprising eight million navigation samples, encompassing diverse robotic platforms and varied navigation tasks. To handle the varying camera configurations across platforms and the temporal span of tasks, NavFoM incorporates identifier tokens to embed both view information and temporal context. Furthermore, to accommodate computational resource constraints in practical deployments, the model employs a sampling strategy that dynamically adjusts observation tokens within a limited token length budget. Evaluation results demonstrate that NavFoM achieves leading or highly competitive performance across seven public benchmarks without requiring task-specific fine-tuning. Real-world robotic experiments further validate its robust generalization capabilities and practical applicability.", "questions": "see weekness", "limitations": "", "rating": 10, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The paper introduces the core concept of a “cross-vehicle, cross-task navigation foundation model,” demonstrating high innovation and foresight. It addresses the limitation in current embodied AI navigation research where models are often confined to specific vehicles or tasks, pointing toward a promising direction for constructing universal navigation agents.\n2.The paper's technical solution for unifying multi-vehicle, multi-task inputs—using identifier tokens to embed camera view information and task temporal context—is remarkably clever and direct. The dynamic token sampling strategy also demonstrates deep consideration for practical deployment constraints.\n3.Extensive testing across seven public benchmarks and real-world scenarios covering diverse robots and tasks provides robust validation of NavFoModel's effectiveness and generalization capabilities, lending strong credibility to the results.\n4.The research does not confine itself to idealized laboratory settings but explicitly addresses and tackles challenges in real-world deployments, such as computational efficiency and adaptability to dynamic environments, thereby enhancing the practical value of the work.\n5.The author provided detailed implementation specifics and stated that the source code would be open-sourced.", "weaknesses": "1.The paper would be significantly strengthened by adding a dedicated analysis of the model's failure modes (e.g., in long-horizon planning or perceptually challenging environments), which is vital for defining the approach's boundaries and guiding future research.​​ \n2.The paper emphasizes the model's practicality and proposes the BATS strategy to optimize computational overhead, but lacks detailed, quantitative analysis of deployment costs.", "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": "NL6ShdmoUK", "reviewer": "Reviewer_VJ2q", "summary": "NavFoM adopts a unified architecture that can handle multimodal inputs from different camera configurations and time spans, making it suitable for tasks across various embodiment types, such as quadruped robots, drones, wheeled robots, and vehicles. These tasks include vision-language navigation, object search, object tracking, and autonomous driving. To address multi-view and temporal contexts, the model introduces Temporal-View Indication (TVI) tokens, which help the model understand and adapt to different camera perspectives and time steps. Through an innovative Budget-Aware Temporal Sampling (BATS) strategy, NavFoM efficiently manages navigation history information with limited computational resources, balancing performance and inference speed. Moreover, NavFoM achieves state-of-the-art performance on multiple public benchmarks and demonstrates strong generalization capabilities across various embodiments and tasks, particularly excelling without the need for task-specific fine-tuning. Through practical testing on platforms such as humanoid robots, quadruped robots, drones, and wheeled robots, the paper validates the model's potential for real-world applications, showing that NavFoM can effectively handle complex navigation tasks with broad practical applicability.", "questions": "1. The output of the model is two-dimensional. How can we control the drone to navigate in a three-dimensional space?\n2. Will using the latest model, such as Qwen3 VL, lead to performance improvement?", "limitations": "", "rating": 8, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.Unified Architecture: The model processes multimodal navigation inputs from different camera configurations and task horizons. It uses temporal-viewpoint indicator tokens (TVI tokens) to accommodate diverse camera setups and time contexts in navigation tasks. This allows the model to handle various visual inputs dynamically.\n\n2.Budget-Aware Temporal Sampling (BATS): This method addresses real-world deployment challenges, such as memory and inference speed, by dynamically sampling navigation history based on a token budget. This ensures the model balances performance with resource constraints.\n\n3.Generalization Across Tasks and Embodiments: NavFoM achieves state-of-the-art (SOTA) performance across seven public benchmarks and in real-world experiments without requiring task-specific fine-tuning. The model demonstrates significant improvements, particularly in multi-camera settings, and shows robust performance in diverse environments such as robotics, drones, and autonomous vehicles.", "weaknesses": "A large language model based approach might fail to handle highly dynamic environments.", "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": "EZHvVlXpn6", "reviewer": "Reviewer_xMYL", "summary": "The paper presents NavFoM, a unified foundation model for cross-embodiment and cross-task embodied navigation. The model is designed to handle diverse camera setups (single or multi-view) and temporal contexts using two key mechanisms: Temporal–Viewpoint Indicator (TVI) tokens that encode spatiotemporal context, and Budget-Aware Temporal Sampling (BATS) that efficiently selects observation tokens under computational constraints.\n\nOverall, this is a well-motivated and timely contribution that advances the goal of general-purpose embodied AI by unifying perception, language, and control within a single framework.", "questions": "1. Figure 3:Could the authors clarify whether Figure 3 is an illustration of the proposed TVI tokenization or an empirical plot generated from a specific task? If it represents actual performance data, additional details on the experimental setup and interpretation would be appreciated.\n2. Fine-tuning: Have the authors evaluated NavFoM under a fine-tuning regime on at least one representative navigation task? Reporting such results would help assess how well the foundation model adapts to task-specific settings and whether it can surpass specialised policies after adaptation.\n3. Clarification on data proportions in Figure 7: The description of “x% others + specific data” in Figure 7 is somewhat ambiguous. Could the authors specify the absolute scale of “other” versus “specific” data used to construct each mixture? Without this information, it is difficult to interpret the proportional relationships or judge whether performance improvements stem from data diversity or data volume.\n4. Interactions between BATS and TVI: While Figure 8 presents independent ablations of the BATS and TVI components, it would be valuable to investigate their combined or hybrid variants (e.g., Linear Probability Sampling + Individually Learned Special Tokens). Have the authors explored whether these mechanisms exhibit complementary effects when jointly optimised?\n5. Relative contribution of TVI and BATS: From the reported ablations, TVI appears to contribute more significantly to performance consistency: models including TVI often perform comparably well even when alternative sampling schemes replace BATS. Is this interpretation accurate, and can the authors comment on why TVI might play a more critical role in the unified framework?\n6. Evaluation in Table 4: In the NavTest results, was NavFoM fine-tuned on the NavSim dataset, or are these results based on zero-shot transfer? Please clarify how the comparisons to baseline models were normalised to ensure an equitable evaluation (e.g., identical data exposure, observation viewpoints, training epochs, or evaluation protocols).\n7. Latency and on-board scalability: Given that navigation tasks often require real-time control, could the authors provide quantitative information about inference latency and computational scaling? Insights into how NavFoM performs under constrained hardware or whether lightweight variants (e.g., distilled or quantised versions) are feasible would help evaluate its deployability in practical robotic systems.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. NavFoM is trained on a large-scale, diverse dataset encompassing multiple embodiments and navigation-related tasks. This scale enables effective transfer and cross-task generalisation.\n\n2. The introduction of TVI tokens and the BATS sampling scheme are well-motivated, domain-specific innovations that address the unique challenges of temporal-spatial consistency and token efficiency in navigation settings.\n\n3. By using a single backbone to handle instruction-following, object search, and driving tasks, the paper takes a meaningful step toward a generalist embodied navigation model. The paper reports competitive or state-of-the-art performance across multiple benchmarks and demonstrates successful deployment on real robots.", "weaknesses": "1. The real-world experiments rely on remote inference using an RTX-4090 GPU, as the model is too large to be hosted on typical on-board computing hardware. While this approach is understandable given current hardware limitations, it limits the practical deployability of the proposed system. Exploring model compression, distillation, or quantisation strategies could make NavFoM more suitable for real-time, on-board navigation scenarios. The reviewer acknowledges that such challenges are common in large foundation models but believes they warrant explicit discussion in the paper.\n2. \n* The performance gains reported in Table 1 are often modest relative to baselines, and the paper prioritises the success rate (SR) metric in the main text while other metrics receive minimal attention. A clearer explanation of the relative importance and interpretation of these metrics would improve transparency. \n* Additionally, for metric-related discussion the readers are referred to the appendix. The appendix provides only a brief description of the metrics, mainly full-form expansions without equations or methodological details. Including formal definitions and clarifying what each metric measures would help readers better assess the quantitative improvements.\n3. The paper does not explicitly discuss limitations, failure modes, or robustness issues, which is especially important for large foundation models deployed in real-world environments. Acknowledging potential failure cases (e.g., under sensor noise, unseen environments, or dynamic obstacles) and discussing safety implications would provide a more balanced and credible presentation of the work.\n4. While the paper presents ablations on TVI and BATS components, the analysis remains somewhat surface-level. The interactions between these modules, their computational overhead, and their contribution to overall model complexity are not extensively studied. A more detailed examination (for example, isolating how each mechanism influences learning dynamics or token efficiency) would strengthen the methodological understanding and reproducibility of the work.", "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": "3HqxpvzVT8", "reviewer": "Reviewer_iLxn", "summary": "The paper proposes a cross-task, cross-embodiment navigation foundation model that ingests egocentric multi-view video plus language and outputs waypoint trajectories, trained on 8M navigation samples spanning quadrupeds, drones, wheeled robots, and vehicles. \n\nIts key innovations are Temporal-Viewpoint Indicator tokens to encode camera view and time, and a Budget-Aware Temporal Sampling scheme that keeps recent context while respecting a token budget.", "questions": "An ablation study on the loss weights can help validate the effectiveness of the fixed hyperparameters.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper introduce Temporal-Viewpoint Indicator, which provide explicit view and time encoding so the LLM can align frames across cameras, and Budget-Aware Temporal Sampling preserves recent context under a fixed token budget.\n* Across seven public benchmarks and additional real-world robot trials, NavFoM attains state-of-the-art or highly competitive results without task-specific fine-tuning.", "weaknesses": "* The model fine-tunes only a single epoch, this suggests possible under-optimization given the 12.7M sample corpus.\n* Multi-task training uses a fixed loss weight 𝜙 = 10 to scale navigation MSE against QA loss, the paper doesn’t probe sensitivity to this choice.", "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)