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class="data"></p></div></a></div><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://ucc-ie.academia.edu/DonRoss"><img class="profile-avatar u-positionAbsolute" alt="Don Ross" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/34228/11192/19836440/s200_don.ross.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://ucc-ie.academia.edu/DonRoss">Don Ross</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University College Cork</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://academicmedicalcentreuniversiteitvanamsterdam.academia.edu/JulianKiverstein"><img class="profile-avatar u-positionAbsolute" alt="Julian Kiverstein" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/37257/12353/11592/s200_julian.kiverstein.jpeg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://academicmedicalcentreuniversiteitvanamsterdam.academia.edu/JulianKiverstein">Julian Kiverstein</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Academic Medical Centre/ Universiteit van Amsterdam</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://aucegypt.academia.edu/MahmoudFarag"><img class="profile-avatar u-positionAbsolute" alt="Mahmoud Farag" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/71133/9442544/18245442/s200_mahmoud.farag.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://aucegypt.academia.edu/MahmoudFarag">Mahmoud Farag</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">American University in Cairo</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://ksu.academia.edu/DavidSeamon"><img class="profile-avatar u-positionAbsolute" alt="David Seamon" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/93547/25922/29662134/s200_david.seamon.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://ksu.academia.edu/DavidSeamon">David Seamon</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Kansas State University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://uni-koln.academia.edu/RemoCaponi"><img class="profile-avatar u-positionAbsolute" alt="Remo Caponi" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/103326/28437/67684825/s200_remo.caponi.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://uni-koln.academia.edu/RemoCaponi">Remo Caponi</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of Cologne</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://um-boston.academia.edu/MariaBrincker"><img class="profile-avatar u-positionAbsolute" alt="Maria Brincker" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/129303/34449/710484/s200_maria.brincker.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://um-boston.academia.edu/MariaBrincker">Maria Brincker</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of Massachusetts, Boston</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://unipr.academia.edu/VittorioGallese"><img class="profile-avatar u-positionAbsolute" alt="Vittorio Gallese" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/138943/921229/156149242/s200_vittorio.gallese.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://unipr.academia.edu/VittorioGallese">Vittorio Gallese</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Università degli Studi di Parma (Italy)</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://columbia.academia.edu/JoshuaMailman"><img class="profile-avatar u-positionAbsolute" alt="Joshua B Mailman" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/279897/54429/50176/s200_joshua_b..mailman.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://columbia.academia.edu/JoshuaMailman">Joshua B Mailman</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Columbia University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://southerndenmark.academia.edu/StephenJCowley"><img class="profile-avatar u-positionAbsolute" alt="Stephen J. Cowley" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/347902/464803/588087/s200_stephen_j..cowley.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://southerndenmark.academia.edu/StephenJCowley">Stephen J. Cowley</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of Southern Denmark</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://oxfordbrookes.academia.edu/FabioCuzzolin"><img class="profile-avatar u-positionAbsolute" alt="Fabio Cuzzolin" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/366407/112374/61740579/s200_fabio.cuzzolin.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://oxfordbrookes.academia.edu/FabioCuzzolin">Fabio Cuzzolin</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Oxford Brookes University</p></div></div></ul></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="41514875" href="https://www.academia.edu/Documents/in/Dynamical_Systems_Approach_to_Cognition"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/GeorgMartius","location":"/GeorgMartius","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/GeorgMartius","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Dynamical Systems Approach to Cognition"]}" data-trace="false" data-dom-id="Pill-react-component-f2930853-9ea1-4e6d-9b30-f62845c9b35f"></div> <div id="Pill-react-component-f2930853-9ea1-4e6d-9b30-f62845c9b35f"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="41514875" href="https://www.academia.edu/Documents/in/Autonomous_Robotics"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Autonomous Robotics"]}" data-trace="false" data-dom-id="Pill-react-component-d5d53156-1cdb-485f-bb87-d0e448ee9a7d"></div> <div id="Pill-react-component-d5d53156-1cdb-485f-bb87-d0e448ee9a7d"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="41514875" href="https://www.academia.edu/Documents/in/Neuroscience"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Neuroscience"]}" data-trace="false" data-dom-id="Pill-react-component-7a3dce77-97f5-42ca-8557-98ae802ebe96"></div> <div id="Pill-react-component-7a3dce77-97f5-42ca-8557-98ae802ebe96"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="41514875" href="https://www.academia.edu/Documents/in/Robotics"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Robotics"]}" data-trace="false" data-dom-id="Pill-react-component-12614792-c1b1-45bf-afd8-7ad190e3f677"></div> <div id="Pill-react-component-12614792-c1b1-45bf-afd8-7ad190e3f677"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="41514875" href="https://www.academia.edu/Documents/in/Self_Organization"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Self Organization"]}" data-trace="false" data-dom-id="Pill-react-component-807137ae-8e58-4a19-be3e-08249c701364"></div> <div id="Pill-react-component-807137ae-8e58-4a19-be3e-08249c701364"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Georg Martius</h3></div><div class="js-work-strip profile--work_container" data-work-id="33854447"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/33854447/Quantifying_Self_Organizing_Behavior_of_Autonomous_Robots"><img alt="Research paper thumbnail of Quantifying Self-Organizing Behavior of Autonomous Robots" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/33854447/Quantifying_Self_Organizing_Behavior_of_Autonomous_Robots">Quantifying Self-Organizing Behavior of Autonomous Robots</a></div><div class="wp-workCard_item"><span>07/20/2015-07/24/2015</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="33854447"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="33854447"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33854447; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33854447]").text(description); $(".js-view-count[data-work-id=33854447]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33854447; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='33854447']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + 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Self-exploration of the Stumpy Robot with Predictive Information Maximization" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/33854446/Self_exploration_of_the_Stumpy_Robot_with_Predictive_Information_Maximization">Self-exploration of the Stumpy Robot with Predictive Information Maximization</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT One of the long-term goals of artificial life research is to create autonomous, self-mot...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT One of the long-term goals of artificial life research is to create autonomous, self-motivated, and intelligent animats. We study an intrinsic motivation system for behavioral self-exploration based on the maximization of the predictive information using the Stumpy robot, which is the first evaluation of the algorithm on a real robot. The control is organized in a closed-loop fashion with a reactive controller that is subject to fast synaptic dynamics. Even though the available sensors of the robot produce very noisy and peaky signals, the self-exploration algorithm was successful and various emerging behaviors were observed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="33854446"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="33854446"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33854446; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=33854443]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":33854443,"title":"Goal-oriented control of self-organizing behavior in autonomous robots","internal_url":"https://www.academia.edu/33854443/Goal_oriented_control_of_self_organizing_behavior_in_autonomous_robots","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="33854442"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/33854442/Variants_of_guided_self_organization_for_robot_control"><img alt="Research paper thumbnail of Variants of guided self-organization for robot control" class="work-thumbnail" src="https://attachments.academia-assets.com/53834445/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/33854442/Variants_of_guided_self_organization_for_robot_control">Variants of guided self-organization for robot control</a></div><div class="wp-workCard_item"><span>Theory in Biosciences</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Autonomous robots can generate exploratory behavior by self-organization of the sensorimotor loop...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Autonomous robots can generate exploratory behavior by self-organization of the sensorimotor loop. We show that the behavioral manifold that is covered in this way can be modified in a goal-dependent way without reducing the self-induced activity of the robot. We present three strategies for guided self-organization, namely by using external rewards, a problem-specific error function, or assumptions about the symmetries of the desired behavior. The strategies are analyzed for two different robots in a physically realistic simulation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="77c1bdda7a2dd5eabf0bab37d76d233c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53834445,"asset_id":33854442,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53834445/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="33854442"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="33854442"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33854442; 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33854440]").text(description); $(".js-view-count[data-work-id=33854440]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33854440; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='33854440']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=33854440]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":33854440,"title":"Robustness of Guided Self-Organization Against Sensorimotor Disruptions","internal_url":"https://www.academia.edu/33854440/Robustness_of_Guided_Self_Organization_Against_Sensorimotor_Disruptions","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="33854439"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/33854439/Tipping_the_scales_Guidance_and_intrinsically_motivated_behavior"><img alt="Research paper thumbnail of Tipping the scales: Guidance and intrinsically motivated behavior" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/33854439/Tipping_the_scales_Guidance_and_intrinsically_motivated_behavior">Tipping the scales: Guidance and intrinsically motivated behavior</a></div><div class="wp-workCard_item"><span>Proceedings of advances in artificial life, 11th European Conference (ECAL 2011). MIT Press</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="33854439"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="33854439"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33854439; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33854439]").text(description); $(".js-view-count[data-work-id=33854439]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33854439; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='33854439']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=33854439]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":33854439,"title":"Tipping the scales: Guidance and intrinsically motivated behavior","internal_url":"https://www.academia.edu/33854439/Tipping_the_scales_Guidance_and_intrinsically_motivated_behavior","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282146"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282146/Information_driven_intrinsic_motivation_in_reinforcement_learning"><img alt="Research paper thumbnail of Information-driven intrinsic motivation in reinforcement learning" class="work-thumbnail" src="https://attachments.academia-assets.com/41177781/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282146/Information_driven_intrinsic_motivation_in_reinforcement_learning">Information-driven intrinsic motivation in reinforcement learning</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the main challenges in the field of embodied artificial intelligence is the open-ended aut...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One of the main challenges in the field of embodied artificial intelligence is the open-ended autonomous learning of complex behaviours. Our approach is to use task-independent, information-driven intrinsic motivation(s) to support task-dependent learning. The work presented here is a preliminary step in which we investigate the predictive information (the mutual information of the past and future of the sensor stream) as an intrinsic drive, ideally supporting any kind of task acquisition. Previous experiments have shown that the predictive information (PI) is a good candidate to support autonomous, open-ended learning of complex behaviours, because a maximisation of the PI corresponds to an exploration of morphology-and environment-dependent behavioural regularities. The idea is that these regularities can then be exploited in order to solve any given task. Three different experiments are presented and their results lead to the conclusion that the linear combination of the one-step PI with an external reward function is not generally recommended in an episodic policy gradient setting. Only for hard tasks a great speed-up can be achieved at the cost of an asymptotic performance lost. Keywords: information-driven self-organisation, predictive information, reinforcement learning, embodied artificial intelligence, embodied machine learning controller . A similar learning rule was obtained from the principle of Homeokinesis . In both cases a gradient information was derived to pursue local optimisation. For the integration of external goals a set of methods have been proposed Martius and Herrmann [2012], which however cannot deal with the standard reinforcement setting of arbitrary time-delayed rewards that we study here. Prokopenko et al. [2006] used the PI, estimated on the spatio-temporal phase-space of an embodied system, as part of fitness function in an artificial evolution setting. It was shown that the resulting locomotion behaviour of a snake-bot was more robust, compared to the setting, in which only the travelled distance determined the fitness.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c6662a6039c63546e857ea605e4c0cc7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41177781,"asset_id":20282146,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41177781/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282146"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282146"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282146; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282145"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282145/Converting_DNA_to_Music_ComposAlign"><img alt="Research paper thumbnail of Converting DNA to Music: ComposAlign" class="work-thumbnail" src="https://attachments.academia-assets.com/41177871/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282145/Converting_DNA_to_Music_ComposAlign">Converting DNA to Music: ComposAlign</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Alignments are part of the most important data type in the field of comparative genomics. They ca...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Alignments are part of the most important data type in the field of comparative genomics. They can be abstracted to a character matrix derived from aligned sequences. A variety of biological questions forces the researcher to inspect these alignments. Our tool, called COMPOSALIGN, was developed to sonify large scale genomic data. The resulting musical composition is based on COMMON MUSIC and allows the mapping of genes to motifs and species to instruments. It enables the researcher to listen to the musical representation of the genome-wide alignment and contrasts a bioinformatician's sight-oriented work at the computer.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="27928cdc63b45c9cd3cf03d67f2a4b14" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41177871,"asset_id":20282145,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41177871/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282145"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282145"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282145; 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20282144]").text(description); $(".js-view-count[data-work-id=20282144]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20282144; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20282144']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=20282144]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20282144,"title":"preprint2013 60","internal_url":"https://www.academia.edu/20282144/preprint2013_60","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282143"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282143/From_Motor_Babbling_to_Purposive_Actions_Emerging_Self_exploration_in_a_Dynamical_Systems_Approach_to_Early_Robot_Development"><img alt="Research paper thumbnail of From Motor Babbling to Purposive Actions: Emerging Self-exploration in a Dynamical Systems Approach to Early Robot Development" class="work-thumbnail" src="https://attachments.academia-assets.com/41177872/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282143/From_Motor_Babbling_to_Purposive_Actions_Emerging_Self_exploration_in_a_Dynamical_Systems_Approach_to_Early_Robot_Development">From Motor Babbling to Purposive Actions: Emerging Self-exploration in a Dynamical Systems Approach to Early Robot Development</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Self-organization and the phenomenon of emergence play an essential role in living systems and fo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Self-organization and the phenomenon of emergence play an essential role in living systems and form a challenge to artificial life systems. This is not only because systems become more lifelike, but also since self-organization may help in reducing the design efforts in creating complex behavior systems. The present paper studies self-exploration based on a general approach to the self-organization of behavior, which has been developed and tested in various examples in recent years. This is a step towards autonomous early robot development. We consider agents under the close sensorimotor coupling paradigm with a certain cognitive ability realized by an internal forward model. Starting from tabula rasa initial conditions we overcome the bootstrapping problem and show emerging self-exploration. Apart from that, we analyze the effect of limited actions, which lead to deprivation of the world model. We show that our paradigm explicitly avoids this by producing purposive actions in a natural way. Examples are given using a simulated simple wheeled robot and a spherical robot driven by shifting internal masses.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f071bc2b418ca68a57402498b7b78279" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41177872,"asset_id":20282143,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41177872/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282143"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282143"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282143; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282142"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20282142/Behavior_as_broken_symmetry_in_embodied_self_organizing_robots"><img alt="Research paper thumbnail of Behavior as broken symmetry in embodied self-organizing robots" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/20282142/Behavior_as_broken_symmetry_in_embodied_self_organizing_robots">Behavior as broken symmetry in embodied self-organizing robots</a></div><div class="wp-workCard_item"><span>Advances in Artificial Life, ECAL 2013</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282142"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282142"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282142; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=20282142]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20282142,"title":"Behavior as broken symmetry in embodied self-organizing robots","internal_url":"https://www.academia.edu/20282142/Behavior_as_broken_symmetry_in_embodied_self_organizing_robots","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282141"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282141/Taming_the_Beast_Guided_Self_organization_of_Behavior_in_Autonomous_Robots"><img alt="Research paper thumbnail of Taming the Beast: Guided Self-organization of Behavior in Autonomous Robots" class="work-thumbnail" src="https://attachments.academia-assets.com/41344259/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282141/Taming_the_Beast_Guided_Self_organization_of_Behavior_in_Autonomous_Robots">Taming the Beast: Guided Self-organization of Behavior in Autonomous Robots</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Self-organizing processes are crucial for the development of living beings. Practical application...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Self-organizing processes are crucial for the development of living beings. Practical applications in robots may benefit from the selforganization of behavior, e.g. for the increased fault tolerance and enhanced flexibility provided that external goals can also be achieved. We present several methods for the guidance of self-organizing control by externally prescribed criteria. We show that the degree of self-organized explorativity of the robot can be regulated and that problem-specific error functions, hints, or abstract symbolic descriptions of a goal can be reconciled with the continuous robot dynamics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1f5193bd0e5814aba3b074668e17f73e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41344259,"asset_id":20282141,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41344259/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282141"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282141"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282141; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "1f5193bd0e5814aba3b074668e17f73e" } } $('.js-work-strip[data-work-id=20282141]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20282141,"title":"Taming the Beast: Guided Self-organization of Behavior in Autonomous Robots","internal_url":"https://www.academia.edu/20282141/Taming_the_Beast_Guided_Self_organization_of_Behavior_in_Autonomous_Robots","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[{"id":41344259,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41344259/thumbnails/1.jpg","file_name":"taming_the_beast_martius2010.pdf","download_url":"https://www.academia.edu/attachments/41344259/download_file","bulk_download_file_name":"Taming_the_Beast_Guided_Self_organizatio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41344259/taming_the_beast_martius2010-libre.pdf?1453284194=\u0026response-content-disposition=attachment%3B+filename%3DTaming_the_Beast_Guided_Self_organizatio.pdf\u0026Expires=1740614601\u0026Signature=JtV4ZrQ3Se3uIOrfNEyk7sN6Y8So03mqaXwCMHAkLyu2iYeeW-ODou1KJIjoZqDrYxhe7~9q4NDdn6xlYNCXBB7b-1bN7jhCQWb5rH3rQJ3sQM0AJ9N5rcYBFZXK1koLnQSb3z5Ba9knNIZJBuLJDX-H-DxIPTT8Ky~pwAvZh1SlM6BUIBS2DO~YwchhZhelJ~~6Yhjy8uyGQekPVw5G4pqplXk1ixx3oNfQfseu4djVkA71KlpkIyt9gMALPZTxuV5o~RcaEiqOIpo5Dhc9yy6jCc9wkalCDXNiG4Q8vzErUuA1yqSB4~3YUigSkXCJRmYKufKL4MUWo3n4fpEsIA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282140"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282140/Emergence_of_Interaction_among_Adaptive_Agents"><img alt="Research paper thumbnail of Emergence of Interaction among Adaptive Agents" class="work-thumbnail" src="https://attachments.academia-assets.com/41344153/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282140/Emergence_of_Interaction_among_Adaptive_Agents">Emergence of Interaction among Adaptive Agents</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Robotic agents can self-organize their interaction with the environment by an adaptive "homeokine...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Robotic agents can self-organize their interaction with the environment by an adaptive "homeokinetic" controller that simultaneously maximizes sensitivity of the behavior and predictability of sensory inputs. Based on previous work with single robots, we study the interaction of two homeokinetic agents. We show that this paradigm also produces quasi-social interactions among artificial agents. The results suggest that homeokinetic learning generates social behavior only in the the context of an actual encounter of the interaction partner while this does not happen for an identical stimulus pattern that is only replayed. This is in agreement with earlier experiments with human subjects. Greek: homoios (equally, likewise) and kinesis (movement), meaning the adaptive control of a kinetic quantity and thereby self-regulate internal parameters.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0dd19c54856eaf00c8610ef089326ef4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41344153,"asset_id":20282140,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41344153/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282140; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0dd19c54856eaf00c8610ef089326ef4" } } $('.js-work-strip[data-work-id=20282140]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20282140,"title":"Emergence of Interaction among Adaptive Agents","internal_url":"https://www.academia.edu/20282140/Emergence_of_Interaction_among_Adaptive_Agents","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[{"id":41344153,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41344153/thumbnails/1.jpg","file_name":"martius.et.al.2008.pdf","download_url":"https://www.academia.edu/attachments/41344153/download_file","bulk_download_file_name":"Emergence_of_Interaction_among_Adaptive.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41344153/martius.et.al.2008-libre.pdf?1453283891=\u0026response-content-disposition=attachment%3B+filename%3DEmergence_of_Interaction_among_Adaptive.pdf\u0026Expires=1740614601\u0026Signature=Z5KrxH~GhLKMOOUhg-JTHE1Cvhs9akEvQpw3vb3C3rwszHMMY5iC2fImZWeI6iD9wxKv8rOB4jfiM-GzL5m-2dfgRTFqlgscQ4GioACqpcK~s8Y0kU6el6mOScZC3SWfV9BddUbQCRWP~WA~gCPC-9KJk19JjjKUHJeyws1wurEZqd9-FG50F7dx2Mqx5X5U9~W9sDiK-Lazn1t52dBx24CXzu168itxHUCEguoIztyspgI7aG6xNWyXPE~7L0WxI0FJmt3ND-WBTYzDHPN1Frr2oWvAQ-cloAmWi8BpPVBPIAPbmYPxXsV0asw0xeLsWlPdfbXr-CHYqwuikfjEPQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282139"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282139/Guided_Self_organisation_for_Autonomous_Robot_Development"><img alt="Research paper thumbnail of Guided Self-organisation for Autonomous Robot Development" class="work-thumbnail" src="https://attachments.academia-assets.com/41344766/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282139/Guided_Self_organisation_for_Autonomous_Robot_Development">Guided Self-organisation for Autonomous Robot Development</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The paper presents a method to guide the self-organised development of behaviours of autonomous r...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The paper presents a method to guide the self-organised development of behaviours of autonomous robots. In earlier publications we demonstrated how to use the homeokinesis principle and dynamical systems theory to obtain self-organised playful but goal-free behaviour. Now we extend this framework by reinforcement signals. We validate the mechanisms with two experiment with a spherical robot. The first experiment aims at fast motion, where the robot reaches on average about twice the speed of a not reinforcement robot. In the second experiment spinning motion is rewarded and we demonstrate that the robot successfully develops pirouettes and curved motion which only rarely occur among the natural behaviours of the robot.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="52a561183e8c058b462d2ccf30cce32c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41344766,"asset_id":20282139,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41344766/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282139"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282139"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282139; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282138"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20282138/Robot_Learning_by_Guided_Self_Organization"><img alt="Research paper thumbnail of Robot Learning by Guided Self-Organization" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/20282138/Robot_Learning_by_Guided_Self_Organization">Robot Learning by Guided Self-Organization</a></div><div class="wp-workCard_item"><span>Emergence, Complexity and Computation</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282138"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282138"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282138; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=20282137]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20282137,"title":"Facing the Unknown — Homeokinesis in a New Representation","internal_url":"https://www.academia.edu/20282137/Facing_the_Unknown_Homeokinesis_in_a_New_Representation","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282136"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20282136/Low_Dimensional_Robotic_Systems"><img alt="Research paper thumbnail of Low Dimensional Robotic Systems" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/20282136/Low_Dimensional_Robotic_Systems">Low Dimensional Robotic Systems</a></div><div class="wp-workCard_item"><span>Cognitive Systems Monographs</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282136"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282136"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282136; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20282136]").text(description); $(".js-view-count[data-work-id=20282136]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20282136; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20282136']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33854447]").text(description); $(".js-view-count[data-work-id=33854447]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33854447; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='33854447']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=33854447]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":33854447,"title":"Quantifying Self-Organizing Behavior of Autonomous Robots","internal_url":"https://www.academia.edu/33854447/Quantifying_Self_Organizing_Behavior_of_Autonomous_Robots","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="33854446"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/33854446/Self_exploration_of_the_Stumpy_Robot_with_Predictive_Information_Maximization"><img alt="Research paper thumbnail of Self-exploration of the Stumpy Robot with Predictive Information Maximization" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/33854446/Self_exploration_of_the_Stumpy_Robot_with_Predictive_Information_Maximization">Self-exploration of the Stumpy Robot with Predictive Information Maximization</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT One of the long-term goals of artificial life research is to create autonomous, self-mot...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT One of the long-term goals of artificial life research is to create autonomous, self-motivated, and intelligent animats. We study an intrinsic motivation system for behavioral self-exploration based on the maximization of the predictive information using the Stumpy robot, which is the first evaluation of the algorithm on a real robot. The control is organized in a closed-loop fashion with a reactive controller that is subject to fast synaptic dynamics. Even though the available sensors of the robot produce very noisy and peaky signals, the self-exploration algorithm was successful and various emerging behaviors were observed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="33854446"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="33854446"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33854446; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=33854443]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":33854443,"title":"Goal-oriented control of self-organizing behavior in autonomous robots","internal_url":"https://www.academia.edu/33854443/Goal_oriented_control_of_self_organizing_behavior_in_autonomous_robots","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="33854442"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/33854442/Variants_of_guided_self_organization_for_robot_control"><img alt="Research paper thumbnail of Variants of guided self-organization for robot control" class="work-thumbnail" src="https://attachments.academia-assets.com/53834445/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/33854442/Variants_of_guided_self_organization_for_robot_control">Variants of guided self-organization for robot control</a></div><div class="wp-workCard_item"><span>Theory in Biosciences</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Autonomous robots can generate exploratory behavior by self-organization of the sensorimotor loop...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Autonomous robots can generate exploratory behavior by self-organization of the sensorimotor loop. We show that the behavioral manifold that is covered in this way can be modified in a goal-dependent way without reducing the self-induced activity of the robot. We present three strategies for guided self-organization, namely by using external rewards, a problem-specific error function, or assumptions about the symmetries of the desired behavior. The strategies are analyzed for two different robots in a physically realistic simulation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="77c1bdda7a2dd5eabf0bab37d76d233c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53834445,"asset_id":33854442,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53834445/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="33854442"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="33854442"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33854442; 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33854440]").text(description); $(".js-view-count[data-work-id=33854440]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33854440; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='33854440']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=33854440]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":33854440,"title":"Robustness of Guided Self-Organization Against Sensorimotor Disruptions","internal_url":"https://www.academia.edu/33854440/Robustness_of_Guided_Self_Organization_Against_Sensorimotor_Disruptions","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="33854439"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/33854439/Tipping_the_scales_Guidance_and_intrinsically_motivated_behavior"><img alt="Research paper thumbnail of Tipping the scales: Guidance and intrinsically motivated behavior" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/33854439/Tipping_the_scales_Guidance_and_intrinsically_motivated_behavior">Tipping the scales: Guidance and intrinsically motivated behavior</a></div><div class="wp-workCard_item"><span>Proceedings of advances in artificial life, 11th European Conference (ECAL 2011). MIT Press</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="33854439"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="33854439"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33854439; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33854439]").text(description); $(".js-view-count[data-work-id=33854439]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33854439; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='33854439']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=33854439]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":33854439,"title":"Tipping the scales: Guidance and intrinsically motivated behavior","internal_url":"https://www.academia.edu/33854439/Tipping_the_scales_Guidance_and_intrinsically_motivated_behavior","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282146"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282146/Information_driven_intrinsic_motivation_in_reinforcement_learning"><img alt="Research paper thumbnail of Information-driven intrinsic motivation in reinforcement learning" class="work-thumbnail" src="https://attachments.academia-assets.com/41177781/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282146/Information_driven_intrinsic_motivation_in_reinforcement_learning">Information-driven intrinsic motivation in reinforcement learning</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the main challenges in the field of embodied artificial intelligence is the open-ended aut...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One of the main challenges in the field of embodied artificial intelligence is the open-ended autonomous learning of complex behaviours. Our approach is to use task-independent, information-driven intrinsic motivation(s) to support task-dependent learning. The work presented here is a preliminary step in which we investigate the predictive information (the mutual information of the past and future of the sensor stream) as an intrinsic drive, ideally supporting any kind of task acquisition. Previous experiments have shown that the predictive information (PI) is a good candidate to support autonomous, open-ended learning of complex behaviours, because a maximisation of the PI corresponds to an exploration of morphology-and environment-dependent behavioural regularities. The idea is that these regularities can then be exploited in order to solve any given task. Three different experiments are presented and their results lead to the conclusion that the linear combination of the one-step PI with an external reward function is not generally recommended in an episodic policy gradient setting. Only for hard tasks a great speed-up can be achieved at the cost of an asymptotic performance lost. Keywords: information-driven self-organisation, predictive information, reinforcement learning, embodied artificial intelligence, embodied machine learning controller . A similar learning rule was obtained from the principle of Homeokinesis . In both cases a gradient information was derived to pursue local optimisation. For the integration of external goals a set of methods have been proposed Martius and Herrmann [2012], which however cannot deal with the standard reinforcement setting of arbitrary time-delayed rewards that we study here. Prokopenko et al. [2006] used the PI, estimated on the spatio-temporal phase-space of an embodied system, as part of fitness function in an artificial evolution setting. It was shown that the resulting locomotion behaviour of a snake-bot was more robust, compared to the setting, in which only the travelled distance determined the fitness.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c6662a6039c63546e857ea605e4c0cc7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41177781,"asset_id":20282146,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41177781/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282146"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282146"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282146; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282145"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282145/Converting_DNA_to_Music_ComposAlign"><img alt="Research paper thumbnail of Converting DNA to Music: ComposAlign" class="work-thumbnail" src="https://attachments.academia-assets.com/41177871/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282145/Converting_DNA_to_Music_ComposAlign">Converting DNA to Music: ComposAlign</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Alignments are part of the most important data type in the field of comparative genomics. They ca...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Alignments are part of the most important data type in the field of comparative genomics. They can be abstracted to a character matrix derived from aligned sequences. A variety of biological questions forces the researcher to inspect these alignments. Our tool, called COMPOSALIGN, was developed to sonify large scale genomic data. The resulting musical composition is based on COMMON MUSIC and allows the mapping of genes to motifs and species to instruments. It enables the researcher to listen to the musical representation of the genome-wide alignment and contrasts a bioinformatician's sight-oriented work at the computer.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="27928cdc63b45c9cd3cf03d67f2a4b14" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41177871,"asset_id":20282145,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41177871/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282145"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282145"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282145; 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20282144]").text(description); $(".js-view-count[data-work-id=20282144]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20282144; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20282144']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=20282144]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20282144,"title":"preprint2013 60","internal_url":"https://www.academia.edu/20282144/preprint2013_60","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282143"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282143/From_Motor_Babbling_to_Purposive_Actions_Emerging_Self_exploration_in_a_Dynamical_Systems_Approach_to_Early_Robot_Development"><img alt="Research paper thumbnail of From Motor Babbling to Purposive Actions: Emerging Self-exploration in a Dynamical Systems Approach to Early Robot Development" class="work-thumbnail" src="https://attachments.academia-assets.com/41177872/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282143/From_Motor_Babbling_to_Purposive_Actions_Emerging_Self_exploration_in_a_Dynamical_Systems_Approach_to_Early_Robot_Development">From Motor Babbling to Purposive Actions: Emerging Self-exploration in a Dynamical Systems Approach to Early Robot Development</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Self-organization and the phenomenon of emergence play an essential role in living systems and fo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Self-organization and the phenomenon of emergence play an essential role in living systems and form a challenge to artificial life systems. This is not only because systems become more lifelike, but also since self-organization may help in reducing the design efforts in creating complex behavior systems. The present paper studies self-exploration based on a general approach to the self-organization of behavior, which has been developed and tested in various examples in recent years. This is a step towards autonomous early robot development. We consider agents under the close sensorimotor coupling paradigm with a certain cognitive ability realized by an internal forward model. Starting from tabula rasa initial conditions we overcome the bootstrapping problem and show emerging self-exploration. Apart from that, we analyze the effect of limited actions, which lead to deprivation of the world model. We show that our paradigm explicitly avoids this by producing purposive actions in a natural way. Examples are given using a simulated simple wheeled robot and a spherical robot driven by shifting internal masses.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f071bc2b418ca68a57402498b7b78279" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41177872,"asset_id":20282143,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41177872/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282143"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282143"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282143; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282142"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20282142/Behavior_as_broken_symmetry_in_embodied_self_organizing_robots"><img alt="Research paper thumbnail of Behavior as broken symmetry in embodied self-organizing robots" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/20282142/Behavior_as_broken_symmetry_in_embodied_self_organizing_robots">Behavior as broken symmetry in embodied self-organizing robots</a></div><div class="wp-workCard_item"><span>Advances in Artificial Life, ECAL 2013</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282142"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282142"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282142; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=20282142]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20282142,"title":"Behavior as broken symmetry in embodied self-organizing robots","internal_url":"https://www.academia.edu/20282142/Behavior_as_broken_symmetry_in_embodied_self_organizing_robots","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282141"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282141/Taming_the_Beast_Guided_Self_organization_of_Behavior_in_Autonomous_Robots"><img alt="Research paper thumbnail of Taming the Beast: Guided Self-organization of Behavior in Autonomous Robots" class="work-thumbnail" src="https://attachments.academia-assets.com/41344259/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282141/Taming_the_Beast_Guided_Self_organization_of_Behavior_in_Autonomous_Robots">Taming the Beast: Guided Self-organization of Behavior in Autonomous Robots</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Self-organizing processes are crucial for the development of living beings. Practical application...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Self-organizing processes are crucial for the development of living beings. Practical applications in robots may benefit from the selforganization of behavior, e.g. for the increased fault tolerance and enhanced flexibility provided that external goals can also be achieved. We present several methods for the guidance of self-organizing control by externally prescribed criteria. We show that the degree of self-organized explorativity of the robot can be regulated and that problem-specific error functions, hints, or abstract symbolic descriptions of a goal can be reconciled with the continuous robot dynamics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1f5193bd0e5814aba3b074668e17f73e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41344259,"asset_id":20282141,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41344259/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282141"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282141"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282141; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "1f5193bd0e5814aba3b074668e17f73e" } } $('.js-work-strip[data-work-id=20282141]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20282141,"title":"Taming the Beast: Guided Self-organization of Behavior in Autonomous Robots","internal_url":"https://www.academia.edu/20282141/Taming_the_Beast_Guided_Self_organization_of_Behavior_in_Autonomous_Robots","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[{"id":41344259,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41344259/thumbnails/1.jpg","file_name":"taming_the_beast_martius2010.pdf","download_url":"https://www.academia.edu/attachments/41344259/download_file","bulk_download_file_name":"Taming_the_Beast_Guided_Self_organizatio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41344259/taming_the_beast_martius2010-libre.pdf?1453284194=\u0026response-content-disposition=attachment%3B+filename%3DTaming_the_Beast_Guided_Self_organizatio.pdf\u0026Expires=1740614601\u0026Signature=JtV4ZrQ3Se3uIOrfNEyk7sN6Y8So03mqaXwCMHAkLyu2iYeeW-ODou1KJIjoZqDrYxhe7~9q4NDdn6xlYNCXBB7b-1bN7jhCQWb5rH3rQJ3sQM0AJ9N5rcYBFZXK1koLnQSb3z5Ba9knNIZJBuLJDX-H-DxIPTT8Ky~pwAvZh1SlM6BUIBS2DO~YwchhZhelJ~~6Yhjy8uyGQekPVw5G4pqplXk1ixx3oNfQfseu4djVkA71KlpkIyt9gMALPZTxuV5o~RcaEiqOIpo5Dhc9yy6jCc9wkalCDXNiG4Q8vzErUuA1yqSB4~3YUigSkXCJRmYKufKL4MUWo3n4fpEsIA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282140"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282140/Emergence_of_Interaction_among_Adaptive_Agents"><img alt="Research paper thumbnail of Emergence of Interaction among Adaptive Agents" class="work-thumbnail" src="https://attachments.academia-assets.com/41344153/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282140/Emergence_of_Interaction_among_Adaptive_Agents">Emergence of Interaction among Adaptive Agents</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Robotic agents can self-organize their interaction with the environment by an adaptive "homeokine...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Robotic agents can self-organize their interaction with the environment by an adaptive "homeokinetic" controller that simultaneously maximizes sensitivity of the behavior and predictability of sensory inputs. Based on previous work with single robots, we study the interaction of two homeokinetic agents. We show that this paradigm also produces quasi-social interactions among artificial agents. The results suggest that homeokinetic learning generates social behavior only in the the context of an actual encounter of the interaction partner while this does not happen for an identical stimulus pattern that is only replayed. This is in agreement with earlier experiments with human subjects. Greek: homoios (equally, likewise) and kinesis (movement), meaning the adaptive control of a kinetic quantity and thereby self-regulate internal parameters.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0dd19c54856eaf00c8610ef089326ef4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41344153,"asset_id":20282140,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41344153/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282140; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0dd19c54856eaf00c8610ef089326ef4" } } $('.js-work-strip[data-work-id=20282140]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20282140,"title":"Emergence of Interaction among Adaptive Agents","internal_url":"https://www.academia.edu/20282140/Emergence_of_Interaction_among_Adaptive_Agents","owner_id":41514875,"coauthors_can_edit":true,"owner":{"id":41514875,"first_name":"Georg","middle_initials":null,"last_name":"Martius","page_name":"GeorgMartius","domain_name":"independent","created_at":"2016-01-15T00:29:08.787-08:00","display_name":"Georg Martius","url":"https://independent.academia.edu/GeorgMartius"},"attachments":[{"id":41344153,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41344153/thumbnails/1.jpg","file_name":"martius.et.al.2008.pdf","download_url":"https://www.academia.edu/attachments/41344153/download_file","bulk_download_file_name":"Emergence_of_Interaction_among_Adaptive.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41344153/martius.et.al.2008-libre.pdf?1453283891=\u0026response-content-disposition=attachment%3B+filename%3DEmergence_of_Interaction_among_Adaptive.pdf\u0026Expires=1740614601\u0026Signature=Z5KrxH~GhLKMOOUhg-JTHE1Cvhs9akEvQpw3vb3C3rwszHMMY5iC2fImZWeI6iD9wxKv8rOB4jfiM-GzL5m-2dfgRTFqlgscQ4GioACqpcK~s8Y0kU6el6mOScZC3SWfV9BddUbQCRWP~WA~gCPC-9KJk19JjjKUHJeyws1wurEZqd9-FG50F7dx2Mqx5X5U9~W9sDiK-Lazn1t52dBx24CXzu168itxHUCEguoIztyspgI7aG6xNWyXPE~7L0WxI0FJmt3ND-WBTYzDHPN1Frr2oWvAQ-cloAmWi8BpPVBPIAPbmYPxXsV0asw0xeLsWlPdfbXr-CHYqwuikfjEPQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20282139"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282139/Guided_Self_organisation_for_Autonomous_Robot_Development"><img alt="Research paper thumbnail of Guided Self-organisation for Autonomous Robot Development" class="work-thumbnail" src="https://attachments.academia-assets.com/41344766/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282139/Guided_Self_organisation_for_Autonomous_Robot_Development">Guided Self-organisation for Autonomous Robot Development</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The paper presents a method to guide the self-organised development of behaviours of autonomous r...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The paper presents a method to guide the self-organised development of behaviours of autonomous robots. In earlier publications we demonstrated how to use the homeokinesis principle and dynamical systems theory to obtain self-organised playful but goal-free behaviour. Now we extend this framework by reinforcement signals. We validate the mechanisms with two experiment with a spherical robot. The first experiment aims at fast motion, where the robot reaches on average about twice the speed of a not reinforcement robot. In the second experiment spinning motion is rewarded and we demonstrate that the robot successfully develops pirouettes and curved motion which only rarely occur among the natural behaviours of the robot.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="52a561183e8c058b462d2ccf30cce32c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41344766,"asset_id":20282139,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41344766/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282139"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282139"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282139; 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