[2411.01796] Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge

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In CHAIC, the goal is for an embodied agent equipped with egocentric observations to assist a human who may be operating under physical constraints -- e.g., unable to reach high places or confined to a wheelchair -- in performing common household or outdoor tasks as efficiently as possible. To achieve this, a successful helper must: (1) infer the human's intents and constraints by following the human and observing their behaviors (social perception), and (2) make a cooperative plan tailored to the human partner to solve the task as quickly as possible, working together as a team (cooperative planning). To benchmark this challenge, we create four new agents with real physical constraints and eight long-horizon tasks featuring both indoor and outdoor scenes with various constraints, emergency events, and potential risks. We benchmark planning- and learning-based baselines on the challenge and introduce a new method that leverages large language models and behavior modeling. Empirical evaluations demonstrate the effectiveness of our benchmark in enabling systematic assessment of key aspects of machine social intelligence. Our benchmark and code are publicly available at https://github.com/UMass-Foundation-Model/CHAIC."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Constrained Human-AI Cooperation: An Inclusive Embodied Social..."/> <meta name="twitter:description" content="We introduce Constrained Human-AI Cooperation (CHAIC), an inclusive embodied social intelligence challenge designed to test social perception and cooperation in embodied agents. In CHAIC, the goal..."/> <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="Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge" /><meta name="citation_author" content="Du, Weihua" /><meta name="citation_author" content="Lyu, Qiushi" /><meta name="citation_author" content="Shan, Jiaming" /><meta name="citation_author" content="Qi, Zhenting" /><meta name="citation_author" content="Zhang, Hongxin" /><meta name="citation_author" content="Chen, Sunli" /><meta name="citation_author" content="Peng, Andi" /><meta name="citation_author" content="Shu, Tianmin" /><meta name="citation_author" content="Lee, Kwonjoon" /><meta name="citation_author" content="Dariush, Behzad" /><meta name="citation_author" content="Gan, Chuang" /><meta name="citation_date" content="2024/11/04" /><meta name="citation_online_date" content="2024/11/05" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2411.01796" /><meta name="citation_arxiv_id" content="2411.01796" /><meta name="citation_abstract" content="We introduce Constrained Human-AI Cooperation (CHAIC), an inclusive embodied social intelligence challenge designed to test social perception and cooperation in embodied agents. In CHAIC, the goal is for an embodied agent equipped with egocentric observations to assist a human who may be operating under physical constraints -- e.g., unable to reach high places or confined to a wheelchair -- in performing common household or outdoor tasks as efficiently as possible. To achieve this, a successful helper must: (1) infer the human's intents and constraints by following the human and observing their behaviors (social perception), and (2) make a cooperative plan tailored to the human partner to solve the task as quickly as possible, working together as a team (cooperative planning). To benchmark this challenge, we create four new agents with real physical constraints and eight long-horizon tasks featuring both indoor and outdoor scenes with various constraints, emergency events, and potential risks. We benchmark planning- and learning-based baselines on the challenge and introduce a new method that leverages large language models and behavior modeling. Empirical evaluations demonstrate the effectiveness of our benchmark in enabling systematic assessment of key aspects of machine social intelligence. Our benchmark and code are publicly available at https://github.com/UMass-Foundation-Model/CHAIC." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a>  <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" href="{url_path('ignore_me')}"></a> <div class="header-breadcrumbs is-hidden-mobile"> <a 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href="https://arxiv.org/search/cs?searchtype=author&query=Du,+W" rel="nofollow">Weihua Du</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lyu,+Q" rel="nofollow">Qiushi Lyu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shan,+J" rel="nofollow">Jiaming Shan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qi,+Z" rel="nofollow">Zhenting Qi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+H" rel="nofollow">Hongxin Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+S" rel="nofollow">Sunli Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Peng,+A" rel="nofollow">Andi Peng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shu,+T" rel="nofollow">Tianmin Shu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+K" rel="nofollow">Kwonjoon Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dariush,+B" rel="nofollow">Behzad Dariush</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gan,+C" rel="nofollow">Chuang Gan</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge, by Weihua Du and 10 other authors</div> <a class="mobile-submission-download" href="/pdf/2411.01796">View PDF</a> <a class="mobile-submission-download" href="https://arxiv.org/html/2411.01796v2">HTML (experimental)</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>We introduce Constrained Human-AI Cooperation (CHAIC), an inclusive embodied social intelligence challenge designed to test social perception and cooperation in embodied agents. In CHAIC, the goal is for an embodied agent equipped with egocentric observations to assist a human who may be operating under physical constraints -- e.g., unable to reach high places or confined to a wheelchair -- in performing common household or outdoor tasks as efficiently as possible. To achieve this, a successful helper must: (1) infer the human's intents and constraints by following the human and observing their behaviors (social perception), and (2) make a cooperative plan tailored to the human partner to solve the task as quickly as possible, working together as a team (cooperative planning). To benchmark this challenge, we create four new agents with real physical constraints and eight long-horizon tasks featuring both indoor and outdoor scenes with various constraints, emergency events, and potential risks. We benchmark planning- and learning-based baselines on the challenge and introduce a new method that leverages large language models and behavior modeling. Empirical evaluations demonstrate the effectiveness of our benchmark in enabling systematic assessment of key aspects of machine social intelligence. Our benchmark and code are publicly available at <a href="https://github.com/UMass-Foundation-Model/CHAIC" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </blockquote>  <div class="metatable"> <table summary="Additional metadata"> <tr> <td class="tablecell label">Comments:</td> <td class="tablecell comments mathjax">NeurIPS 2024 Dataset and Benchmark Track. The first two authors contributed equally. Project Website at <a href="https://vis-www.cs.umass.edu/CHAIC/" 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">Artificial Intelligence (cs.AI)</span>; Human-Computer Interaction (cs.HC); Robotics (cs.RO)</td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2411.01796">arXiv:2411.01796</a> [cs.AI]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2411.01796v2">arXiv:2411.01796v2</a> [cs.AI]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2411.01796" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2411.01796</a><div class="button-and-tooltip"> 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[2411.01796] Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge