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<!DOCTYPE html>
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<h1 class="title is-1 publication-title">RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://geng-haoran.github.io/">Haoran Geng</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://fisher-wang.github.io/">Feishi Wang</a><sup>1,2,3*</sup>,</span>
<span class="author-block">
<a href="https://songlin.github.io/">Songlin Wei</a><sup>2*</sup>,
</span>
<span class="author-block">
<a href="https://yuyangli.com/">Yuyang Li</a><sup>2*</sup>,
</span>
<span class="author-block">
<a href="https://www.bangjunwang.com/">Bangjun Wang</a><sup>3*</sup>,
</span>
<span class="author-block">
<a href="https://boshi-an.github.io/">Boshi An</a><sup>2*</sup>,
</span>
<span class="author-block">
<a href="https://charlietcheng.github.io/">Charlie Tianyue Cheng</a><sup>1*</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=BIPK9KEAAAAJ">Haozhe Lou</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/peihao-li-555660225/">Peihao Li</a><sup>1,4</sup>,
</span>
<span class="author-block">
<a href="https://wangyenjen.github.io/">Yen-Jen Wang</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://www.lyt0112.com/">Yutong Liang</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://dylan-goetting.github.io/">Dylan Goetting</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://co1one.github.io/">Chaoyi Xu</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://blog.chenhaozhe.top/">Haozhe Chen</a><sup>5</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=KmGNBSMAAAAJ">Yuxi Qian</a><sup>6</sup>,
</span>
<span class="author-block">
<a href="https://gengyiran.github.io/">Yiran Geng</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://pointscoder.github.io/">Jiageng Mao</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://wkwan7.github.io/">Weikang Wan</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://robo-alex.github.io/">Mingtong Zhang</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://jiangranlv.github.io/">Jiangran Lyu</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://sihengz02.github.io/">Siheng Zhao</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://jzhzhang.github.io/">Jiazhao Zhang</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://mzhmxzh.github.io/">Jialiang Zhang</a><sup>1,2</sup>,
</span>
<span class="author-block">
<a href="https://chengyzhao.github.io/">Chengyang Zhao</a><sup>7</sup>,
</span>
<span class="author-block">
<a href="https://luhr2003.github.io/">Haoran Lu</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://selina2023.github.io/">Yufei Ding</a><sup>1,2</sup>,
</span>
<span class="author-block">
<a href="https://nikepupu.github.io/">Ran Gong</a><sup>8</sup>,
</span>
<span class="author-block">
<a href="">Yuran Wang</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://yuxuank.com/">Yuxuan Kuang</a><sup>2,3</sup>,
</span>
<span class="author-block">
<a href="https://warshallrho.github.io/">Ruihai Wu</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://buzz-beater.github.io/">Baoxiong Jia</a><sup>9</sup>,
</span>
<span class="author-block">
<a href="https://sferrazza.cc/">Carlo Sferrazza</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://zsdonghao.github.io/">Hao Dong</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://siyuanhuang.com/">Siyuan Huang</a><sup>9†</sup>,
</span>
<span class="author-block">
<a href="https://yuewang.xyz/">Yue Wang</a><sup>3†</sup>,
</span>
<span class="author-block">
<a href="https://people.eecs.berkeley.edu/~malik/">Jitendra Malik</a><sup>1†</sup>,
</span>
<span class="author-block">
<a href="https://people.eecs.berkeley.edu/~pabbeel/">Pieter Abbeel</a><sup>1†</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>* </sup>Equal Contribution</span>
<span class="author-block"><sup>† </sup>Equal Advising</span>
<br>
<span class="author-block"><sup></sup>Corresponding to Haoran Geng (ghr@berkeley.edu)</span>
</div>
<div class="is-size-5 institutions">
<span class="author-block"><sup>1</sup> <img src="./static/images/logo/ucb.png" style="height: 80px; vertical-align: middle; margin-right: 12px;"></span>
<span class="author-block"><sup>2</sup> <img src="./static/images/logo/pku.png" style="height: 50px; vertical-align: middle; margin-right: 12px;"></span>
<span class="author-block"><sup>3</sup><img src="./static/images/logo/usc.png" style="height: 30px; vertical-align: middle; margin-right: 12px; margin-left: 5px;"></span>
<span class="author-block"><sup>4</sup><img src="./static/images/logo/umich.png" style="height: 80px; vertical-align: middle; margin-left: 5px;"></span>
<br>
<span class="author-block"><sup>5</sup><img src="./static/images/logo/uiuc.png" style="height: 30px; vertical-align: middle; margin-right: 12px; margin-left: 5px;"></span>
<span class="author-block"><sup>6</sup><img src="./static/images/logo/stanford_logo.avif" style="height: 35px; vertical-align: middle; margin-right: 5px; margin-left: 5px;"><img src="./static/images/logo/stanford.png" style="height: 30px; vertical-align: middle; margin-right: 12px;"></span>
<span class="author-block"><sup>7</sup><img src="./static/images/logo/cmu_logo.jpg" style="height: 35px; vertical-align: middle; margin-right: 5px; margin-left: 5px;"><img src="./static/images/logo/cmu.png" style="height: 50px; vertical-align: middle; margin-right: 12px;"></span>
<span class="author-block"><sup>8</sup><img src="./static/images/logo/ucla_logo.png" style="height: 35px; vertical-align: middle; margin-left: 5px; margin-right: 5px;"><img src="./static/images/logo/ucla.png" style="height: 30px; vertical-align: middle; margin-right: 12px;"></span>
<span class="author-block"><sup>9</sup><img src="./static/images/logo/bigai.png" style="height: 50px; vertical-align: middle;"></span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
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class="external-link button is-normal is-rounded is-dark">
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<span>Paper</span>
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<!-- Code Link. -->
<span class="link-block">
<a href="https://github.com/RoboVerseOrg/RoboVerse"
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>
<span class="link-block">
<a href="https://roboverse.wiki/"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-book"></i>
</span>
<span>Documentation</span>
</a>
</span>
<!-- Dataset Link. -->
<span class="link-block">
<a href="https://huggingface.co/datasets/RoboVerseOrg/roboverse_data"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-database"></i>
</span>
<span>Data</span>
</a>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<img src="./static/images/teaser.jpg">
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Data scaling and standardized evaluation benchmarks have driven significant advances in natural language processing and computer vision. However, robotics faces unique challenges in scaling data and establishing reliable evaluation protocols. Collecting real-world robotic data is resource-intensive and inefficient, while benchmarking in real-world scenarios remains highly complex. Synthetic data and simulation offer promising alternatives, yet existing efforts often fall short in data quality, diversity, and benchmark standardization.
</p>
<p>
To address these challenges, we introduce <strong>RoboVerse</strong>, a comprehensive framework comprising a <em>simulation platform</em>, a <em>synthetic dataset</em>, and <em>unified benchmarks</em>. Our simulation platform supports multiple simulators and robotic embodiments, enabling seamless transitions between different environments. The synthetic dataset, featuring high-fidelity physics and photorealistic rendering, is constructed through multiple approaches including migration from public datasets, policy rollout, and motion planning, etc. enhanced by data augmentation. Additionally, we propose unified benchmarks for imitation learning and reinforcement learning, enabling consistent evaluation across different levels of generalization.
</p>
<p>
At the core of the <em>simulation platform</em> is <strong>MetaSim</strong>, an infrastructure that abstracts diverse simulation environments into a universal interface. It restructures existing simulation environments into a simulator-agnostic configuration system, as well as an API aligning different simulator functionalities, such as launching simulation environments, loading assets with initial states, stepping the physics engine, etc. This abstraction ensures interoperability and extensibility.
</p>
<p>
Comprehensive experiments demonstrate that RoboVerse enhances the performance of imitation learning, reinforcement learning, and world model learning, improving sim-to-real transfer. These results validate the reliability of our dataset and benchmarks, establishing RoboVerse as a robust solution for advancing simulation-assisted robot learning.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Paper video. -->
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<h2 class="title is-3">Video</h2>
<div class="publication-video">
<iframe src="https://www.youtube.com/embed/tmZ21mdXa5A?rel=0&showinfo=0"
frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
</div>
</div>
</div>
<!--/ Paper video. -->
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- MetaSim -->
<div class="columns is-centered">
<div class="column">
<div class="content">
<h2 class="title is-3">MetaSim Infrastructure</h2>
<p>
<strong>MetaSim</strong> is a unified simulation platform with a universal configuration system and a unified interface, combining existing simulators and rendering engines into a single framework.
</p>
<img src="./static/images/metasim.svg">
</div>
</div>
</div>
<!-- RoboVerse Dataset. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">RoboVerse Dataset</h2>
<div class="content has-text-justified">
<p>
RoboVerse provides a large-scale, high-quality dataset.
</p>
<img src="./static/images/datasets.png">
</div>
</div>
</div>
<!-- RoboVerse Benchmark. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">RoboVerse Benchmark</h2>
<div class="content has-text-justified">
<p>
RoboVerse provides a standardized benchmark for both imitation learning and reinforcement learning.
</p>
<img src="./static/images/benchmark.png">
</div>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@misc{geng2025roboverse,
title={RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning},
author={Haoran Geng and Feishi Wang and Songlin Wei and Yuyang Li and Bangjun Wang and Boshi An and Charlie Tianyue Cheng and Haozhe Lou and Peihao Li and Yen-Jen Wang and Yutong Liang and Dylan Goetting and Chaoyi Xu and Haozhe Chen and Yuxi Qian and Yiran Geng and Jiageng Mao and Weikang Wan and Mingtong Zhang and Jiangran Lyu and Siheng Zhao and Jiazhao Zhang and Jialiang Zhang and Chengyang Zhao and Haoran Lu and Yufei Ding and Ran Gong and Yuran Wang and Yuxuan Kuang and Ruihai Wu and Baoxiong Jia and Carlo Sferrazza and Hao Dong and Siyuan Huang and Yue Wang and Jitendra Malik and Pieter Abbeel},
year={2025},
eprint={2504.18904},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2504.18904},
}</code></pre>
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