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[2410.21229] HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots

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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. 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For example, navigation..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots" /><meta name="citation_author" content="He, Tairan" /><meta name="citation_author" content="Xiao, Wenli" /><meta name="citation_author" content="Lin, Toru" /><meta name="citation_author" content="Luo, Zhengyi" /><meta name="citation_author" content="Xu, Zhenjia" /><meta name="citation_author" content="Jiang, Zhenyu" /><meta name="citation_author" content="Kautz, Jan" /><meta name="citation_author" content="Liu, Changliu" /><meta name="citation_author" content="Shi, Guanya" /><meta name="citation_author" content="Wang, Xiaolong" /><meta name="citation_author" content="Fan, Linxi" /><meta name="citation_author" content="Zhu, Yuke" /><meta name="citation_date" content="2024/10/28" /><meta name="citation_online_date" content="2024/10/28" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2410.21229" /><meta name="citation_arxiv_id" content="2410.21229" /><meta name="citation_abstract" content="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." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a> <!-- start desktop header --> <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" 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<strong>arXiv:2410.21229</strong> (cs) </div> <link rel="stylesheet" type="text/css" href="/static/base/1.0.1/css/abs.css"> <div id="content-inner"> <div id="abs"> <div class="dateline"> [Submitted on 28 Oct 2024]</div> <h1 class="title mathjax"><span class="descriptor">Title:</span>HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots</h1> <div class="authors"><span class="descriptor">Authors:</span><a href="https://arxiv.org/search/cs?searchtype=author&amp;query=He,+T" rel="nofollow">Tairan He</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Xiao,+W" rel="nofollow">Wenli Xiao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Lin,+T" rel="nofollow">Toru Lin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Luo,+Z" rel="nofollow">Zhengyi Luo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Xu,+Z" rel="nofollow">Zhenjia Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Jiang,+Z" rel="nofollow">Zhenyu Jiang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Kautz,+J" rel="nofollow">Jan Kautz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Liu,+C" rel="nofollow">Changliu Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Shi,+G" rel="nofollow">Guanya Shi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Wang,+X" rel="nofollow">Xiaolong Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Fan,+L" rel="nofollow">Linxi Fan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Zhu,+Y" rel="nofollow">Yuke Zhu</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots, by Tairan He and 11 other authors</div> <a class="mobile-submission-download" href="/pdf/2410.21229">View PDF</a> <a class="mobile-submission-download" href="https://arxiv.org/html/2410.21229v1">HTML (experimental)</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>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. </blockquote> <!--CONTEXT--> <div class="metatable"> <table summary="Additional metadata"> <tr> <td class="tablecell label">Comments:</td> <td class="tablecell comments mathjax">Project Page: see <a href="https://hover-versatile-humanoid.github.io/" rel="external noopener nofollow" class="link-external link-https">this https URL</a></td> </tr> <tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Robotics (cs.RO)</span></td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2410.21229">arXiv:2410.21229</a> [cs.RO]</span></td> </tr> <tr> <td class="tablecell label">&nbsp;</td> <td class="tablecell arxividv">(or <span class="arxivid"> <a 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