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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
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<title>InternVLA-N1: An Open Dual-System Vision-Language Navigation Foundation Model with Learned Latent Plans</title>
<link rel="icon" type="image/x-icon" href="static/images/internrobotic_logo.webp">
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<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title"> InternVLA-N1: An Open Dual-System Vision-Language Navigation Foundation Model with Learned Latent Plans </h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block"><a href="https://github.com/InternRobotics">Intern Robotics</a>, <a href="https://www.shlab.org.cn/">Shanghai AI Laboratory</a></span>
</div>
<div class="column is-11">
<div style="display: flex; align-items: center; justify-content: center;">
<img src="./static/images/InternRobotics0.png"
class="interpolation-image"
alt="Interpolate start reference image."
style="width:20%; margin-right: 0em;"/>
<img src="./static/images/ailab.png"
class="interpolation-image"
alt="Interpolate start reference image."
style="width:15%; margin-left: 1em;"/>
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<div class="column has-text-centered">
<div class="publication-links">
<!-- ArXiv abstract Link -->
<span class="link-block">
<a href="./static/pdfs/InternVLA_N1.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
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<a href="https://youtu.be/MYZIYvo_8qw"
class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-youtube"></i>
</span>
<span>Video</span>
</a>
</span>
<span class="link-block">
<a href="https://huggingface.co/InternRobotics/InternVLA-N1"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="Hugging Face" style="height: 1em;">
</span>
<span>Model</span>
</a>
</span>
<span class="link-block">
<a href="https://huggingface.co/datasets/InternRobotics/InternData-N1"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-database"></i>
</span>
<span>Dataset</span>
</a>
</span>
<!-- Github link -->
<span class="link-block">
<a href="https://github.com/InternRobotics/InternNav" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Teaser video-->
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<video poster="" id="tree" autoplay controls muted loop height="100%">
<!-- Your video here -->
<source src="./static/videos/supplimentary_demo_compressed.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
</h2>
</div>
</div>
</section>
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<!-- Paper abstract -->
<section class="section hero is-light">
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
We introduce InternVLA-N1, the first open dual-system vision-language navigation foundation model.
Unlike previous navigation foundation models that can only take short-term actions from a limited discrete space, InternVLA-N1 decouples the task as pixel-goal planning with System 2 and agile execution with System 1.
A curriculum two-stage training paradigm is devised for this framework: First, two systems are pretrained with explicit pixel goals as supervision or condition. Subsequently, we freeze System 2 and finetune the newly added latent plans with System 1 in an asynchronous end-to-end manner. Such a paradigm relying on latent plans as the intermediate representation removes the ambiguity of pixel goal planning and provides new potentials for pretraining extensions with video prediction.
To enable scalable training, we develop an efficient navigation data generation pipeline and introduce InternData-N1, the largest navigation dataset to date. InternData-N1 comprises over 50 million egocentric images collected from more than 3,000 scenes, amounting to 4,839 kilometers of robot navigation experience.
We evaluate InternVLA-N1 across 6 challenging navigation benchmarks, where it consistently achieves state-of-the-art performance, with improvements ranging from 3% to 28%.
In particular, it demonstrates synergistic integration of long-horizon planning (>150m) and real-time decision-making (>30Hz) capabilities and can be zero-shot generalized across diverse embodiments (wheeled, quadruped, humanoid) and in-the-wild environments.
All code, models, and datasets are publicly available.
</p>
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</section>
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<!-- Image carousel -->
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<!-- Framework. -->
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-11">
<h2 class="title is-3">Approach</h2>
<img src="./static/images/s1s2_overview.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-11">
<div class="content has-text-justified">
<p>
InternVLA-N1 adopts a compositional architecture featuring a dual-system design that synergistically combines high-level instruction interpretation with low-level action execution. Specifically, our system integrates:
</p>
<ul>
<li><strong>System 2:</strong> A vision-language model (VLM)-based planning module that interprets navigation instructions to predict mid-term waypoint goals through image-grounded reasoning. By predicting pixel coordinates in the image space, it effectively connects instruction understanding with spatial reasoning, enabling long-horizon navigation instruction following.</li>
<li><strong>System 1:</strong> A multi-modal goal-conditioned diffusion policy guided by latent plan or supported explicit goals, which generates executable short-horizon trajectories conditioned on current observations and the asynchronous latent features from System 2. It enables robust, real-time control and local decision-making in complex environments.</li>
</ul>
<p>
To fully unlock open-world generalization and asynchronous inference capabilities in the dual-system architecture, we design a curriculum training scheme. Initially, each system is trained separately to acquire basic navigation skills using explicit goals in a synchronized setting. Then, a joint fine-tuning phase is introduced. In this phase, we incorporate learnable tokens into System 2 as implicit midterm goals to reduce the ambiguity of pixel-based targets.
</p>
</div>
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-11">
<img src="./static/images/internvla_model.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
</div>
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</section>
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<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
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<h2 class="title is-3">InternData-N1 Dataset</h2>
<div class="content has-text-justified">
<p>
For the navigation task, most real-world datasets are constrained by scene diversity and scale. Meanwhile, Internet video datasets suffer from imprecise localization and mapping information, which limits their feasibility as reliable navigation datasets for trajectory prediction. In contrast, we propose efficient pipelines for generating navigation datasets in simulation, aiming to facilitate scalable training.
</p>
</div>
</div>
</div>
<div class="columns is-centered has-text-centered">
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class="interpolation-image"
alt="Interpolate start reference image."/>
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<img src="./static/images/interndata_overview.jpg"
class="interpolation-image"
alt="Interpolate start reference image."/>
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<img src="./static/images/interndata_pipeline.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
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</div>
</div>
</section>
<!-- Video carousel -->
<section class="hero is-small is light">
<div class="hero-body">
<div class="container is-centered has-text-centered">
<hr>
<h1 class="title is-3">Realworld evaluation</h1>
<h2 class="title is-3">Zero-shot transfer to long-horizon navigation</h2>
<div class="columns is-centered is-vcentered">
<div class="column">
<div class="content">
<video poster="" id="video1" autoplay controls muted loop>
<source src="static/videos/v1.mp4" type="video/mp4">
</video>
</div>
</div>
<div class="column">
<div class="content">
<video poster="" id="video2" autoplay controls muted loop>
<source src="static/videos/v2.mp4" type="video/mp4">
</video>
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</div>
<h2 class="title is-3">Clutter Environment Collision Avoidance</h2>
<div class="columns is-centered is-vcentered">
<div class="column">
<div class="video-container">
<video poster="" id="video1" autoplay controls muted loop>
<source src="static/videos/demo_clutter1.mp4" type="video/mp4">
</video>
<p class="has-text-centered instruction-text">Instruction: Walk through the tables and chairs, turn left into the café, and stop at the coffee counter.</p>
</div>
</div>
<div class="column">
<div class="video-container">
<video poster="" id="video2" autoplay controls muted loop>
<source src="static/videos/demo_clutter2.mp4" type="video/mp4">
</video>
<p class="has-text-centered instruction-text">Instruction: Walk through the bamboo forest and turn left. Reach a man sitting on the sofa.</p>
</div>
</div>
</div>
<h2 class="title is-3">Semantics Understanding</h2>
<div class="columns is-centered is-vcentered">
<div class="column">
<div class="video-container">
<video poster="" id="video1" autoplay controls muted loop>
<source src="static/videos/demo_semantic1.mp4" type="video/mp4">
</video>
<p class="has-text-centered instruction-text">Instruction: Walk straight ahead, turn right after seeing the billiard table, and enter the room with shelves.</p>
</div>
</div>
<div class="column">
<div class="video-container">
<video poster="" id="video2" autoplay controls muted loop>
<source src="static/videos/demo_semantic2.mp4" type="video/mp4">
</video>
<p class="has-text-centered instruction-text">Instruction: Enter the office area. Immediately turn left and continue straight. Then make a right turn. Stop at the workstation near the whiteboard.</p>
</div>
</div>
</div>
<h2 class="title is-3"></h2>
<div class="columns is-centered is-vcentered">
<div class="column">
<div class="video-container">
<video poster="" id="video1" autoplay controls muted loop>
<source src="static/videos/demo_semantic3.mp4" type="video/mp4">
</video>
<p class="has-text-centered instruction-text">Instruction: Walk out of this house and find a trash bin.</p>
</div>
</div>
<div class="column">
<div class="video-container">
<video poster="" id="video2" autoplay controls muted loop>
<source src="static/videos/demo_semantic4.mp4" type="video/mp4">
</video>
<p class="has-text-centered instruction-text">Instruction: Walk towards the orange coffee sculpture and go upstairs. Then keep walking straight and turn right at the end. After that turn left and walking to the man with a black umbrella. Stop in front of the doors with red handles.</p>
</div>
</div>
</div>
<hr>
<h2 class="title is-3">Baseline comparison</h2>
<div class="columns is-centered is-vcentered">
<div class="column">
<div class="content">
<video poster="" autoplay controls muted loop>
<source src="static/videos/baseline_navila.mp4" type="video/mp4">
</video>
<p class="has-text-centered is-size-5">NAVILA</p>
</div>
</div>
<div class="column">
<div class="content">
<video poster="" autoplay controls muted loop>
<source src="static/videos/baseline_internrobotics.mp4" type="video/mp4">
</video>
<p class="has-text-centered is-size-5">InternVLA-N1</p>
</div>
</div>
<div class="column">
<div class="content">
<video poster="" autoplay controls muted loop>
<source src="static/videos/baseline_streamvln.mp4" type="video/mp4">
</video>
<p class="has-text-centered is-size-5">StreamVLN</p>
</div>
</div>
</div>
<img src="./static/images/realworld_metrics.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
</div>
</section>
<!-- End video carousel -->
<section class="hero is-small">
<div class="hero-body">
<div class="container is-centered has-text-centered">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<hr>
<h1 class="title is-3">Evaluation in Simulation</h1>
<h2 class="title is-3">Quantitative results</h2>
<div class="content has-text-justified">
<p>
Our RGB-only variant outperforms all previous RGB-based methods on the R2R Val-Unseen benchmark. It achieves a Success Rate (SR) of 55.4% and an SPL of 52.1%, surpassing previous state-of-the-art models like NAVILA and MapNav.
</p>
</div>
<img src="./static/images/main_result.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
<hr>
<div class="content has-text-justified">
<p>
Our System 1 model showcases several distinctive capabilities, outperforming baseline methods significantly. It achieves 2.7x better performance in no-goal exploration tasks, a 10.9% higher success rate in complex indoor path-planning, and 27.1% better performance in image-driven exploration by adaptively balancing exploration and exploitation.
</p>
</div>
<img src="./static/images/s1_result1.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
<img src="./static/images/s1_result2.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
<img src="./static/images/s1_result3.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
</div>
<hr>
<h2 class="title is-3">Dual-System VLN-CE evaluation</h2>
<p>Some visualization results of our dual-system model on VLN-CE benchmark (habitat simulator) are as follows. Green curves are GT.</p>
<div id="results-carousel" class="carousel results-carousel is-centered has-text-centered is-max-desktop content">
<div class="item item-video1">
<video poster="" id="video1" autoplay controls muted loop width="75%">
<!-- Your video file here -->
<source src="static/videos/sim4.mp4"
type="video/mp4">
</video>
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<!-- Your video file here -->
<source src="static/videos/sim5.mp4"
type="video/mp4">
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<video poster="" id="video3" autoplay controls muted loop width="75%">\
<!-- Your video file here -->
<source src="static/videos/sim6.mp4"
type="video/mp4">
</video>
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<video poster="" id="video4" autoplay controls muted loop width="75%">\
<!-- Your video file here -->
<source src="static/videos/sim7.mp4"
type="video/mp4">
</video>
</div>
</div>
<hr>
<h2 class="title is-3">Dual-System VLN-PE evaluation</h2>
<p>To narrow the sim2real gap, we further evaluate our dual-system on VLN-PE with physics simulation of InternUTopia on a Unitree H1 robot. </p>
<div id="results-carousel" class="carousel results-carousel is-centered has-text-centered is-max-desktop content">
<div class="item item-video1">
<video poster="" id="video1" autoplay controls muted loop width="75%">
<!-- Your video file here -->
<source src="static/videos/VLNPE1.mp4"
type="video/mp4">
</video>
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<div class="item item-video2">
<video poster="" id="video2" autoplay controls muted loop width="75%">
<!-- Your video file here -->
<source src="static/videos/VLNPE2.mp4"
type="video/mp4">
</video>
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<video poster="" id="video3" autoplay controls muted loop width="75%">\
<!-- Your video file here -->
<source src="static/videos/VLNPE3.mp4"
type="video/mp4">
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<hr>
<h2 class="title is-3">System 1 evaluation </h2>
<p>Evaluation of our System 1 model in more diverse and clutter environments are shown as follows. Our System 1 supports different input patterns.</p>
<div id="results-carousel" class="carousel results-carousel is-centered has-text-centered is-max-desktop content">
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<source src="static/videos/system1_pointgoal1.mp4" type="video/mp4">
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<h2 class="title is-4" style="margin-top: 1rem;">PointGoal</h2>
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<source src="static/videos/system1_pointgoal2.mp4" type="video/mp4">
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<h2 class="title is-4" style="margin-top: 1rem;">PointGoal</h2>
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<div class="box has-background-white" style="padding: 1.5rem;">
<video poster="" id="s1_video3" autoplay controls muted loop width="75%">
<source src="static/videos/system1_nogoal_1.mp4" type="video/mp4">
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<h2 class="title is-4" style="margin-top: 1rem;">NoGoal</h2>
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<source src="static/videos/system1_nogoal_2.mp4" type="video/mp4">
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<h2 class="title is-4" style="margin-top: 1rem;">NoGoal</h2>
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<div class="box has-background-white" style="padding: 1.5rem;">
<video poster="" id="video1" autoplay controls muted loop width="75%">
<source src="static/videos/system1_imagegoal1.mp4" type="video/mp4">
</video>
<h2 class="title is-4" style="margin-top: 1rem;">ImageGoal</h2>
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<div class="box has-background-white" style="padding: 1.5rem;">
<video poster="" id="video1" autoplay controls muted loop width="75%">
<source src="static/videos/system1_imagegoal2.mp4" type="video/mp4">
</video>
<h2 class="title is-4" style="margin-top: 1rem;">ImageGoal</h2>
</div>
</div>
</div>
</section>
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<h2 class="title">Poster</h2>
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<!--End paper poster -->
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{wang2025internvla,
title = {InternVLA-N1: An Open Dual-System Vision-Language Navigation Foundation Model with Learned Latent Plans},
author = {Intern Robotics},
booktitle = {Arxiv},
year = {2025},
}</code></pre>
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</section>
<!--End BibTex citation -->
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