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[2410.21406] Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation

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One approach is to use state-conditioned action mapping methods to learn mappings between low-dimensional controllers and high DOF manipulators -- prior research suggests these mappings can simplify the teleoperation experience for users. Recent works suggest that neural networks predicting a local linear function are superior to the typical end-to-end multi-layer perceptrons because they allow users to more easily undo actions, providing more control over the system. However, local linear models assume actions exist on a linear subspace and may not capture nuanced actions in training data. We observe that the benefit of these mappings is being an odd function concerning user actions, and propose end-to-end nonlinear action maps which achieve this property. Unfortunately, our experiments show that such modifications offer minimal advantages over previous solutions. We find that nonlinear odd functions behave linearly for most of the control space, suggesting architecture structure improvements are not the primary factor in data-driven teleoperation. Our results suggest other avenues, such as data augmentation techniques and analysis of human behavior, are necessary for action maps to become practical in real-world applications, such as in assistive robotics to improve the quality of life of people living with w disability."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Investigating the Benefits of Nonlinear Action Maps in Data-Driven..."/> <meta name="twitter:description" content="As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay people be able to drive multi-degree-of-freedom platforms..."/> <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="Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation" /><meta name="citation_author" content="Przystupa, Michael" /><meta name="citation_author" content="Gidel, Gauthier" /><meta name="citation_author" content="Taylor, Matthew E." /><meta name="citation_author" content="Jagersand, Martin" /><meta name="citation_author" content="Piater, Justus" /><meta name="citation_author" content="Tosatto, Samuele" /><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/2410.21406" /><meta name="citation_arxiv_id" content="2410.21406" /><meta name="citation_abstract" content="As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay people be able to drive multi-degree-of-freedom platforms with low-dimensional controllers. One approach is to use state-conditioned action mapping methods to learn mappings between low-dimensional controllers and high DOF manipulators -- prior research suggests these mappings can simplify the teleoperation experience for users. Recent works suggest that neural networks predicting a local linear function are superior to the typical end-to-end multi-layer perceptrons because they allow users to more easily undo actions, providing more control over the system. However, local linear models assume actions exist on a linear subspace and may not capture nuanced actions in training data. We observe that the benefit of these mappings is being an odd function concerning user actions, and propose end-to-end nonlinear action maps which achieve this property. Unfortunately, our experiments show that such modifications offer minimal advantages over previous solutions. We find that nonlinear odd functions behave linearly for most of the control space, suggesting architecture structure improvements are not the primary factor in data-driven teleoperation. Our results suggest other avenues, such as data augmentation techniques and analysis of human behavior, are necessary for action maps to become practical in real-world applications, such as in assistive robotics to improve the quality of life of people living with w disability." /> </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" 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class="subheader"> <h1>Computer Science > Robotics</h1> </div> <div class="header-breadcrumbs-mobile"> <strong>arXiv:2410.21406</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>Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation</h1> <div class="authors"><span class="descriptor">Authors:</span><a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Przystupa,+M" rel="nofollow">Michael Przystupa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Gidel,+G" rel="nofollow">Gauthier Gidel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Taylor,+M+E" rel="nofollow">Matthew E. Taylor</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Jagersand,+M" rel="nofollow">Martin Jagersand</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Piater,+J" rel="nofollow">Justus Piater</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Tosatto,+S" rel="nofollow">Samuele Tosatto</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation, by Michael Przystupa and 5 other authors</div> <a class="mobile-submission-download" href="/pdf/2410.21406">View PDF</a> <a class="mobile-submission-download" href="https://arxiv.org/html/2410.21406v1">HTML (experimental)</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay people be able to drive multi-degree-of-freedom platforms with low-dimensional controllers. One approach is to use state-conditioned action mapping methods to learn mappings between low-dimensional controllers and high DOF manipulators -- prior research suggests these mappings can simplify the teleoperation experience for users. Recent works suggest that neural networks predicting a local linear function are superior to the typical end-to-end multi-layer perceptrons because they allow users to more easily undo actions, providing more control over the system. However, local linear models assume actions exist on a linear subspace and may not capture nuanced actions in training data. We observe that the benefit of these mappings is being an odd function concerning user actions, and propose end-to-end nonlinear action maps which achieve this property. Unfortunately, our experiments show that such modifications offer minimal advantages over previous solutions. We find that nonlinear odd functions behave linearly for most of the control space, suggesting architecture structure improvements are not the primary factor in data-driven teleoperation. Our results suggest other avenues, such as data augmentation techniques and analysis of human behavior, are necessary for action maps to become practical in real-world applications, such as in assistive robotics to improve the quality of life of people living with w disability. </blockquote> <!--CONTEXT--> <div class="metatable"> <table summary="Additional metadata"> <tr> <td class="tablecell label">Comments:</td> <td class="tablecell comments mathjax">13 Pages, 7 Figures, presented at Collaborative AI and Modeling of Humans AAAI Bridge Program Submission</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/2410.21406">arXiv:2410.21406</a> [cs.RO]</span></td> </tr> <tr> <td class="tablecell label">&nbsp;</td> <td class="tablecell arxividv">(or <span 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