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<h1 class="title is-1 publication-title">A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<!-- <span class="author-block"><a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Jiazhao Zhang</a><sup>1,2,*</sup>,</span> -->
<span class="author-block">
<a href="https://jzhzhang.github.io/">Jiazhao Zhang</a><sup>1,2</sup>
<a href="https://pku-epic.github.io/Uni-NaVid/">Kunyu Wang</a><sup>3</sup>
<a href="https://wsakobe.github.io/">Shaoan Wang</a><sup>1,2</sup>
<a href="https://pku-epic.github.io/Uni-NaVid/">Minghan Li</a><sup>2</sup>
<a href="https://github.com/lhrrhl0419">Haoran Liu</a><sup>1,2</sup>
</span>
<span class="author-block" style="display: block;">
<a href="https://songlin.github.io/">Songlin Wei</a><sup>1,2</sup>
<a href="https://www.wangzhongyuan.com/">Zhongyuan Wang</a><sup>3</sup>
<a href="https://scholar.google.com/citations?user=X7M0I8kAAAAJ">Zhizheng Zhang</a><sup>2,3,†</sup>
<a href="https://hughw19.github.io/">He Wang</a><sup>1,2,3,†</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<!-- <span class="author-block">Institution Name<br>Conferance name and year</span> -->
<span class="author-block"><sup>1</sup>Peking University
<sup>2</sup>GalBot
<sup>3</sup>Beijing Academy of Artificial Intelligence
</span>
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<div class="is-size-5 publication-authors">
†Equal Advising
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- Arxiv PDF link -->
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<span>Paper</span>
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Robotics: Science and Systems (RSS 2024)
</div> -->
</div>
</div>
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</section>
<section class="hero is-small">
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<h2>
<!-- <h2 class=""> <img src="static/images/highlight_logo.png" width="50"> Highlights</h2> -->
<div class="text-image-container title is-3">
<div>
<img src="static/images/highlight_logo.png" alt="示例图片" width="50">
</div>
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<p>Highlights</p>
</div>
</div>
</h2>
<!-- <p> <b>Real-world demos by following simple instructions, such as walking to a single landmark.</b></p> -->
<ul>
<li><b>Embodied Navigation Task Unification:</b> Uni-NaVid is a navigation generalist integrating various embodied navigation tasks into one model, including Vision-and-Language Navigation (VLN), Object Navigation (ObjectNav), Embodied Question Answering (EQA), and Human-following tasks.</li>
<li><b>Task Synergy Aginst Data Hungry:</b> Uni-NaVid incorporates 3.6 million navigation samples spanning different tasks and achieves significant performance improvements across diverse navigation benchmarks through effective task synergy.</li>
<li><b>High Efficiency and Non-blocking Deployment:</b> Uni-NaVid adopts an online token merging strategy for achieving about 5 Hz model inference, and preficts action for multiple future steps for enabling non-blocking deployment in the real world.</li>
<li><b>Impressive Sim-to-Real Results:</b> Uni-NaVid can generate both actions and languages by combining simulated action data and Internet semantics for a unified cotraining. This exhibits impressive sim-to-real generalizability in real-world environments.</li>
</ul>
</div>
</div>
</section>
<!-- <ul>
<li><b>Unifying Embodied Navigation Tasks:</b> Uni-NaVid is the first video-based Vision-Language Model (VLM) designed to unify diverse navigation tasks, including VLN, ObjectNav, EQA, and human-following tasks.</li>
<li><b>High efficiency VLM that enable a deployment-friendly model:</b> Uni-NaVid processes natural language instructions as input and outputs either actions or textual responses to complete the given instructions.</li>
<li><b>High Efficiency:</b> Uni-NaVid employs an online token merging strategy, achieving nearly 5 Hz inference, which enables non-blocking real-world deployment.</li>
<li><b>Largest General Navigation Dataset:</b> Uni-NaVid includes 3.6 million navigation samples spanning VLN, ObjectNav, EQA, and human-following tasks.</li>
<li><b>Real-World Development Support:</b> Uni-NaVid achieves state-of-the-art performance across general navigation benchmarks and demonstrates strong generalizability in real-world environments.</li>
</ul> -->
<section class="hero is-small">
<div class="hero-body">
<div class="container is-max-desktop has-text-centered">
<h2>
<!-- <h2 class=""> <img src="static/images/highlight_logo.png" width="50"> Highlights</h2> -->
<div class="text-image-container title is-3">
<div>
<img src="static/images/teaser.png" alt="teaser" >
</div>
<!-- <div class="text">
<p>Highlights</p>
</div> -->
</div>
</h2>
</div>
</div>
</section>
<!-- Teaser video 1 -->
<section class="hero is-small">
<div class="hero-body">
<div class="container is-max-desktop has-text-centered">
<h2 class="title is-3">Performance on Compositional Navigation Tasks </h2>
<p> <b>We deploy Uni-Navid in real-world environments to complete compositional instructions for multiple navigation tasks.</b></p>
<!-- <p> <b>Real-world demos by following simple instructions, such as walking to a single landmark.</b></p> -->
<div id="results-carousel-teaser1" class="carousel results-carousel">
<div class="item item-video1_1">
<video poster="" id="video1_1" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/com_navigation/video_1.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video1_3">
<video poster="" id="video18" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/com_navigation/video_3.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video1_5">
<video poster="" id="video19" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/com_navigation/video_5.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video1_9">
<video poster="" id="video9" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/com_navigation/video_9.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</section>
<!-- End teaser video 1 -->
<!-- Teaser video 2 -->
<section class="hero is-small">
<div class="hero-body">
<div class="container is-max-desktop has-text-centered">
<h2 class="title is-3">Performance on Individual Navigation Task </h2>
<p> <b>We deploy Uni-Navid in real-world environments to complete instructions for individual navigation tasks.</b></p>
<!-- <p> <b>Real-world demos by following complex instructions, which consist of several simple instructions.</b></p> -->
<div id="results-carousel-teaser2" class="carousel results-carousel">
<div class="item item-video2_2">
<video poster="" id="video10" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/simple_navigation/video_2.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video2_4">
<video poster="" id="video11" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/simple_navigation/video_4.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video2_6">
<video poster="" id="video12" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/simple_navigation/video_6.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video2_7">
<video poster="" id="video12" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/simple_navigation/video_7.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video2_8">
<video poster="" id="video12" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/simple_navigation/video_8.mp4"
type="video/mp4">
</video>
</div>
</div>
<!-- <h2 class="subtitle has-text-centered">
Real-world demo of our proposed video-based VLM, NaVid, for vision-and-language navigation. Given the human instruction, NaVid only takes online RGB video frames as input and outputs a language action for robotic execution.
</h2> -->
</div>
</div>
</section>
<!-- End teaser video 2 -->
<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
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<div class="column ">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
A practical navigation agent must be capable of handling a wide range of interaction demands, such as following instructions, searching objects, answering questions, tracking people, and more.Existing models for embodied navigation fall short of serving as practical generalists in the real world, as they are often constrained by specific task configurations or pre-defined maps with discretized waypoints. In this work, we present Uni-NaVid, the first video-based vision-language-action (VLA) model designed to unify diverse embodied navigation tasks and enable seamless navigation for mixed long-horizon tasks in unseen real-world environments. Uni-NaVid achieves this by harmonizing the input and output data configurations for all commonly used embodied navigation tasks and thereby integrating all tasks in one model. For training \name, we collect 3.6 million navigation data samples in total from four essential navigation sub-tasks and foster synergy in learning across them. Extensive experiments on comprehensive navigation benchmarks clearly demonstrate the advantages of unification modeling in Uni-NaVid and show it achieves state-of-the-art performance. Additionally, real-world experiments confirm the model's effectiveness and efficiency, shedding light on its strong generalizability.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- YouTube Video-->
<!-- \textbf{Pipeline of \name.} Our method takes only single-view RGB frames $\{\mathbf{x}_1, \cdots, \mathbf{x}_T\}$ and a natural language instruction $\cI$ as input. For each frame, we extract 64 visual tokens using the vision encoder and then use online token merging to accelerate the model while retaining compact visual information. The merged tokens and instruction tokens are sent to the large language model to obtain actions for navigation or answers for embodied question-answering. -->
<!-- The inputs of NaVid consist of the RGB frames from the online video observation {x<sub>0</sub>, · · · , x<sub>t</sub>} along with the human instruction I. For each frame, we use an observation encoder to extract the visual information with the instruction to obtain observation tokens, including, instruction-queried tokens (orange blocks) and instruction-agnostic tokens (blue blocks). At the current step t, the history frames and current frame x<sub>t</sub> are encoded as observation tokens, with 4 and 64 instruction-agnostic tokens for history frames and current frames, respectively. Besides, our method obtains language tokens by a text encoder. Finally, split by the special tokens [HIS], [OBS], and [NAV], we concatenate the observation tokens and language tokens and send the tokens to the Vicuna-7B then obtain the next-step action. -->
<!-- Method Overview -->
<section class="hero is-small">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<!-- <div class="column is-four-fifths"> -->
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<h2 class="title is-3">Method Overview</h2>
<img src="static/images/method.png" alt="NaVid" class="center-image blend-img-background">
<div class="level-set has-text-justified">
<p class="has-text-justified">
<b>The overview of Uni-NaVid.</b> Our method takes only single-view RGB frames { x<sub>1</sub>, · · ·, x<sub>T</sub> } and a natural language instruction as input. For each frame, we extract 64 visual tokens using the vision encoder and then use online token merging to accelerate the model while retaining compact visual information. The merged tokens and instruction tokens are sent to the large language model to obtain actions for navigation or answers for embodied question-answering.
</p>
</div>
</div>
<!-- </div> -->
</div>
</div>
</div>
</section>
<section class="hero is-small">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<!-- <div class="column is-four-fifths"> -->
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<h2 class="title is-3">Data Collection (Navigation 3.6M + VQA 2.3M)</h2>
<img src="static/images/data_ratio.png" alt="NaVid" class="center-image blend-img-background" width="75%">
<div class="level-set has-text-justified">
<p class="has-text-justified">
<div><b style="color: #89a2d4;">Vision-and-Language Navigation
</b>: requires the agent to comprehend language instructions in order to follow the described path. We collect 2.4M samples based on both VLN-CE R2R and VLN-CE RxR.</div>
<div><b style="color: #e9aa7f;">Object Goal Navigation</b>: involves the agent navigating an environment to locate a specific object based on provided visual or linguistic cues. We gather 483k samples from datasets on Habitat Matterport 3D datase</div>
<div><b style="color: #f5d46d;">Embodied Question Answering</b>: requires the agent to navigate to the related area for question answering. We collect 240k video-action samples and 10k video-answering samples on the EQA dataset on Matterport 3D environments.</div>
<div><b style="color: #a5c788;">Human Following</b>: requires the agent to track and follow a human target with a specific description in dynamic and crowded environment. We collect 544k human-following navigation samples.</div>
</p>
<p class="has-text-justified">
<!-- <b>We initialize the encoders and Vicuna-7B using pre-trained weights, and our model requires only one epoch for the training process.</b> -->
</p>
</div>
</div>
<!-- </div> -->
</div>
</div>
</div>
</section>
<!-- End video carousel R2R -->
<section class="hero is-small">
<div class="hero-body">
<div class="container is-max-desktop has-text-centered">
<h2 class="title is-3">Vision-and-Language Navigation </h2>
<h3 class="title is-5">VLN-CE RxR Val-Unseen</h2>
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<h2 class="title is-3"> Object Goal Navigation</h2>
<h2 class="title is-5"> HM3D ObjectNav </h2>
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<h2 class="title is-3">Embodied Question Answering</h2>
<h2 class="title is-5"> MP3D-EQA </h2>
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<h2 class="title is-3">Human Following</h2>
<!-- <h2 class="title is-5"> Requiring the agent to follow a person who satisfies the description. </h2> -->
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<h2 class="title">BibTeX</h2>
<pre><code>@misc{zhang2024uninavid,
title={Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks},
author={Jiazhao Zhang and Kunyu Wang and Shaoan Wang and Minghan Li and Haoran Liu and Songlin Wei and Zhongyuan Wang and Zhizheng Zhang and He Wang},
year={2024},
journal = {arXiv preprint arXiv:2412.06224}
}</code></pre>
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</section>
<!--End BibTex citation -->
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