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Overview ‹ A Taxonomy and Annotation Tool for Common Social Reasoning — MIT Media Lab
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V31.637z M50,35.707H35.714v-7.134H50V35.707z M50.001,42.857v7.144H14.286v-0.003V28.571h14.285v7.136h-7.143v7.15h7.143v-7.143 l7.143-0.006v7.149H50.001z M7.147,7.142h35.706v7.143H7.147V7.142z M50,0H0v50.001h7.143V21.423H50h0.001V0H50z"></path> </svg></span> </a> </li> <li> <a href='/groups/media-lab-research-theme-life-with-ai/overview/'> <span>Media Lab Research Theme: Life with AI</span> </a> </li> </ul> </div> </div> </div> <div class='mobile-companion-rail'> <div class='rail-content'> <div class='rail-content-section'> <nav class='rail-nav'> <ul class='rail-nav-list'> <li class='state-current'><a href='/projects/social-intelligence-benchmark/overview/'>Overview </a></li> <li class=''><a href='/projects/social-intelligence-benchmark/people/'>People </a></li> </ul> <a href='#' id='toggle-358de3e5-1a09-419c-83a7-ccb50bde4457' class='rail-nav-mobile-toggle'> <span class='toggle-open'>More <span class='icon'><svg version='1.1' xmlns='http://www.w3.org/2000/svg' x='0px' y='0px' width='25' 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require(['ui/OpenCloseToggler'], function (OpenCloseToggler) { new OpenCloseToggler({ selector: '#toggle-358de3e5-1a09-419c-83a7-ccb50bde4457', targetSelector: '^.mobile-companion-rail' }); }); }); </script> </nav> </div> </div> </div> <div class='rail-layout-body'> <div class='primary-block'> <div class='primary-block-content'> <div class='banner-group'> </div> <div class='main-copy'> <div> <p>We posit that robots in human-robot interaction, despite differences in embodiment, roles and tasks, require a common set of social reasoning abilities. Hence, we propose a standardized data-driven method for collecting annotations on social errors and competencies across various robotic platforms and interaction scenarios. Therefore, in this paper, we make a call to the community to collect standardized annotations on social errors and competencies. To motivate this need, we bridge the literature between psychology, human-robot-interaction, and general machine learning to explain why the standardized collection of annotations of HRI data across embodiment, roles, and tasks based on social errors and competencies would enable the learning of a common embodiment, task, role agnostic set of social reasoning skills. We conduct an in-depth review surrounding the definitions of social intelligence and the tools developed to measure it, then identify the common central attributes central to social intelligence. Using these attributes, we develop and share a taxonomy of learning signals and an annotation framework that could be used to collect such data. Drawing from the recent technical advances in machine learning, we share ways in which these learning signals could be used in the context of embodied social agents to improve their social behavior.</p> </div> </div> </div> </div> <div class='stream'> </div> <div class='primary-block'> <div class='primary-block-content'> </div> </div> <div data-feed-pagination> <div id='feed-4a926d10-2a7f-4129-a028-6d13a10c0490' class='feed-block module-layout item-container variant-content-type-eyebrows variant-no-glyphs' data-feed-item-type='module'> </div> </div> <script> mlScripts.push(function () { require(['ui/FeedPaginator'], function (FeedPaginator) { new FeedPaginator(); }); }); </script> <footer class='cta-block'> <div>View more</div> <div> <a href='/projects/social-intelligence-benchmark/people/'>people</a> </div> </footer> </div> </div> </div> <footer class='global-footer'> <nav class='global-footer-section variant-sitemap'> <section class='sitemap-section variant-primary'> <div 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