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<link rel="shortcut icon" href="images/apple-touch-ri-logo-white-120x120.png">
<script type="text/javascript" src="js/hidebib.js"></script>
<title>Yihan Dai 代易函</title>
<meta name="Yihan Dai's Homepage" http-equiv="Content-Type" content="Yihan Dai's Homepage">
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<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20">
<tr>
<td width="30%" valign="top"><a href="images/yihandai.jpg"><img src="images/yihandai.jpg" width="100%"
style="border-radius:15px"></a>
<p align=center>
<br />
</p>
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<td width="70%" valign="top" align="justify">
<p align="center">
<pageheading>Yihan Dai 「代易函」</pageheading><br>
</p>
<p>
I am currently completing my senior year at <a href="https://www.whut.edu.cn">Wuhan University of
Technology</a> and will begin my master's
studies at the <a href="http://www.iscas.ac.cn">Institute of Software, Chinese Academy of
Sciences (ISCAS)</a>
next year. My research interests lie
in embodied AI, game agents, and their testing. I am passionate about developing reliable and efficient
agents.
</p>
<p align="center">
<a href="mailto:[email protected]">Email</a> / <a href="https://github.com/yihandai">Github</a>
</p>
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<sectionheading> News</sectionheading>
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<table width="100%" align="center" border="0" cellspacing="0" cellpadding="15">
<ul>
<!-- <li>[11/2024] Received CMU RI Presidential Fellowship. Thanks, CMU!</li> -->
<!-- <li>[07/2024] <a href="https://agile-but-safe.github.io/">ABS</a> is selected as the <a
href="https://roboticsconference.org/2024/program/awards/">Outstanding Student Paper Award Finalist at
RSS 2024</a>!</li>
<li>[04/2024] Invited talk at <a href="https://www.techbeat.net/talk-info?id=864">TechBeat</a>.</li> -->
<!-- <a href="javascript:toggleblock('news')">---- show more ----</a>
<div id="news" style="display:none">
<li>[11/2024] Received CMU RI Presidential Fellowship. Thanks, CMU!</li>
</div> -->
</ul>
</td>
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<hr />
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="10">
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<td>
<sectionheading> Publications</sectionheading>
</td>
</tr>
</table>
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="15">
<!-- <tr>
<td width="40%" valign="top" align="center"><a href="https://hover-versatile-humanoid.github.io/">
<video playsinline autoplay loop muted src="images/hover/HOVER-Teaser-preview-720.mp4"
poster="./images/loading-icon.gif" alt="sym" width="90%"
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<p><a href="https://hover-versatile-humanoid.github.io/" id="HOVER">
<heading>HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots</heading>
</a><br>
Tairan He*, Wenli Xiao*, Toru Lin, Zhengyi Luo, Zhengjia Xu, Zhenyu Jiang, Jan Kautz, Changliu Liu, Guanya
Shi, Xiaolong Wang, Linxi "Jim" Fan†, Yuke Zhu† <br>
2024<br>
</p>
<div class="paper" id="hover">
<a href="https://hover-versatile-humanoid.github.io/">webpage</a> |
<a href="https://hover-versatile-humanoid.github.io/resources/HOVER_paper.pdf">pdf</a> |
<a href="javascript:toggleblock('hover_abs')">abstract</a> |
<a shape="rect" href="javascript:togglebib('hover')" class="togglebib">bibtex</a> |
<a href="https://arxiv.org/abs/2410.21229">arXiv</a>
<p align="justify"> <i id="hover_abs">Humanoid whole-body control requires adapting to diverse tasks such as
navigation, loco-manipulation, and tabletop manipulation, each demanding a different mode of control. For
example, navigation relies on root velocity tracking, while tabletop manipulation prioritizes upper-body
joint angle tracking. Existing approaches typically train individual policies tailored to a specific
command space, limiting their transferability across modes. We present the key insight that full-body
kinematic motion imitation can serve as a common abstraction for all these tasks and provide
general-purpose motor skills for learning multiple modes of whole-body control. Building on this, we
propose HOVER (Humanoid Versatile Controller), a multi-mode policy distillation framework that
consolidates diverse control modes into a unified policy. HOVER enables seamless transitions between
control modes while preserving the distinct advantages of each, offering a robust and scalable solution
for humanoid control across a wide range of modes. By eliminating the need for policy retraining for each
control mode, our approach improves efficiency and flexibility for future humanoid applications.</i></p>
<pre xml:space="preserve">
@article{he2024hover,
title={HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots},
author={He, Tairan and Xiao, Wenli and Lin, Toru and Luo, Zhengyi and Xu, Zhenjia and Jiang, Zhenyu and Kautz, Jan and Liu, Changliu and Shi, Guanya and Wang, Xiaolong and Fan, Linxi and Zhu, Yuke},
journal={arXiv preprint arXiv:2410.21229},
year={2024}
}
</pre>
</div>
</td>
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Website template from <a href="https://tairanhe.com/" here>here</a>
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