{"id":2213,"date":"2023-02-07T14:46:45","date_gmt":"2023-02-07T13:46:45","guid":{"rendered":"https:\/\/www.uni.lu\/fstm-fr\/events\/on-learning-and-optimization-based-methods-for-risk-averse-control-of-autonomous-systems\/"},"modified":"2023-02-07T14:46:45","modified_gmt":"2023-02-07T13:46:45","slug":"on-learning-and-optimization-based-methods-for-risk-averse-control-of-autonomous-systems","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fstm-fr\/events\/on-learning-and-optimization-based-methods-for-risk-averse-control-of-autonomous-systems\/","title":{"rendered":"On Learning- and Optimization-based Methods for Risk-Averse Control of Autonomous Systems"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><p>From energy networks to space systems: complex Autonomous Systems (AS) have become pervasive in our society. In this context, the design of increasingly sophisticated methods for the modeling and control of AS is of utmost relevance, given that they regularly operate in uncertain and dynamic circumstances.<\/p><p>On the one hand, to mitigate hazardous and possibly catastrophic uncertain perturbations during the decision-making (or planning) process, one is led to reliably infuse Learning-based Models (LM) in the control pipeline. LM offers numerous advantages, including accurate representations of sophisticated systems which accomplish complex tasks. Nevertheless, due to the high degree of uncertainty in which AS operates, one must devise LM capable of offering guarantees of reliability.<\/p><p>On the other hand, beneficially leveraging the aforementioned LM for safe-against-uncertainty deployment of AS may come only under specific optimal planning and control processes. In particular, the best trade-off is offered by risk-averse Stochastic Optimal Control Problems (SOCP), providing controls which optimize sophisticated stochastic worst-case-averse costs known as risk measures.<\/p><p>This talk aims at introducing two promising techniques which start bridging the aforementioned gaps. Specifically, the first part of the talk will show how general, i.e., non-linear stochastic differential equations may be estimated through appropriate sampling- and RKHS-based LM, offering high-order error bounds in law. The second part of the talk will address the design of conditions for optimality which can then be leveraged to solve general, i.e., non-smooth risk-averse SOCP through efficient numerical computations.<\/p><p><a href=\"https:\/\/rbonalli.github.io\/\" target=\"_self\" title=\"\" rel=\"noopener\">Dr. Riccardo Bonalli<\/a> Riccardo Bonalli obtained his PhD in applied mathematics from Sorbonne Universit\u00e9 in 2018, in collaboration with ONERA-The French Aerospace Lab. He was a postdoctoral researcher with the Department of Aeronautics and Astronautics, Stanford University. Currently, Riccardo is a tenured CNRS researcher with the Laboratory of Signals and Systems (L2S), at Universit\u00e9 Paris-Saclay. His main research interests concern theoretical and numerical robust optimal control with techniques from differential geometry, statistical analysis, and machine learning and applications in aerospace systems and robotics.<\/p><p>The Machine Learning Seminar is a regular weekly seminar series aiming to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences. More information about the ML Seminar, together with video recordings from past meetings you will find here: <a href=\"https:\/\/legato-team.eu\/seminars\/\" target=\"_self\" title=\"\" rel=\"noopener\">https:\/\/legato-team.eu\/seminars\/<\/a><\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"<p>From energy networks to space systems: complex Autonomous Systems (AS) have become pervasive in our society. In this context, the design of increasingly sophisticated methods for the modeling and control of AS is of utmost relevance, given that they regularly operate in uncertain and dynamic circumstances.On the one hand, to mitigate hazardous and possibly catastrophic uncertain perturbations during the decision-making (or planning) process, one is led to reliably infuse Learning-based Models (LM) in the control pipeline. LM offers numerous advantages, including accurate representations of sophisticated systems which accomplish complex tasks. Nevertheless, due to the high degree of uncertainty in which AS operates, one must devise LM capable of offering guarantees of reliability.On the other hand, beneficially leveraging the aforementioned LM for safe-against-uncertainty deployment of AS may come only under specific optimal planning and control processes. In particular, the best trade-off is offered by risk-averse Stochastic Optimal Control Problems (SOCP), providing controls which optimize sophisticated stochastic worst-case-averse costs known as risk measures.This talk aims at introducing two promising techniques which start bridging the aforementioned gaps. Specifically, the first part of the talk will show how general, i.e., non-linear stochastic differential equations may be estimated through appropriate sampling- and RKHS-based LM, offering high-order error bounds in law. The second part of the talk will address the design of conditions for optimality which can then be leveraged to solve general, i.e., non-smooth risk-averse SOCP through efficient numerical computations.<\/p>\n","protected":false},"author":0,"featured_media":2214,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2023-02-22 10:00:00","event_end_date":"2023-02-22 11:00:00","event_speaker_name":"Dr. Riccardo Bonalli (Laboratory of Signals and Systems, Universit\u00e9 Paris-Saclay, France)","event_speaker_link":"","event_is_online":false,"event_location":"Fully virtual (contact Dr. Jakub Lengiewicz to register)","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[302],"events-type":[],"organisation":[42],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>On Learning- and Optimization-based Methods for Risk-Averse Control of Autonomous Systems - FSTM I Uni.lu<\/title>\n<meta name=\"description\" content=\"From energy networks to space systems: complex Autonomous Systems (AS) have become pervasive in our society. In this context, the design of increasingly sophisticated methods for the modeling and control of AS is of utmost relevance, given that they regularly operate in uncertain and dynamic circumstances.On the one hand, to mitigate hazardous and possibly catastrophic uncertain perturbations during the decision-making (or planning) process, one is led to reliably infuse Learning-based Models (LM) in the control pipeline. LM offers numerous advantages, including accurate representations of sophisticated systems which accomplish complex tasks. Nevertheless, due to the high degree of uncertainty in which AS operates, one must devise LM capable of offering guarantees of reliability.On the other hand, beneficially leveraging the aforementioned LM for safe-against-uncertainty deployment of AS may come only under specific optimal planning and control processes. In particular, the best trade-off is offered by risk-averse Stochastic Optimal Control Problems (SOCP), providing controls which optimize sophisticated stochastic worst-case-averse costs known as risk measures.This talk aims at introducing two promising techniques which start bridging the aforementioned gaps. Specifically, the first part of the talk will show how general, i.e., non-linear stochastic differential equations may be estimated through appropriate sampling- and RKHS-based LM, offering high-order error bounds in law. 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In this context, the design of increasingly sophisticated methods for the modeling and control of AS is of utmost relevance, given that they regularly operate in uncertain and dynamic circumstances.On the one hand, to mitigate hazardous and possibly catastrophic uncertain perturbations during the decision-making (or planning) process, one is led to reliably infuse Learning-based Models (LM) in the control pipeline. LM offers numerous advantages, including accurate representations of sophisticated systems which accomplish complex tasks. Nevertheless, due to the high degree of uncertainty in which AS operates, one must devise LM capable of offering guarantees of reliability.On the other hand, beneficially leveraging the aforementioned LM for safe-against-uncertainty deployment of AS may come only under specific optimal planning and control processes. In particular, the best trade-off is offered by risk-averse Stochastic Optimal Control Problems (SOCP), providing controls which optimize sophisticated stochastic worst-case-averse costs known as risk measures.This talk aims at introducing two promising techniques which start bridging the aforementioned gaps. Specifically, the first part of the talk will show how general, i.e., non-linear stochastic differential equations may be estimated through appropriate sampling- and RKHS-based LM, offering high-order error bounds in law. 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In this context, the design of increasingly sophisticated methods for the modeling and control of AS is of utmost relevance, given that they regularly operate in uncertain and dynamic circumstances.On the one hand, to mitigate hazardous and possibly catastrophic uncertain perturbations during the decision-making (or planning) process, one is led to reliably infuse Learning-based Models (LM) in the control pipeline. LM offers numerous advantages, including accurate representations of sophisticated systems which accomplish complex tasks. Nevertheless, due to the high degree of uncertainty in which AS operates, one must devise LM capable of offering guarantees of reliability.On the other hand, beneficially leveraging the aforementioned LM for safe-against-uncertainty deployment of AS may come only under specific optimal planning and control processes. In particular, the best trade-off is offered by risk-averse Stochastic Optimal Control Problems (SOCP), providing controls which optimize sophisticated stochastic worst-case-averse costs known as risk measures.This talk aims at introducing two promising techniques which start bridging the aforementioned gaps. Specifically, the first part of the talk will show how general, i.e., non-linear stochastic differential equations may be estimated through appropriate sampling- and RKHS-based LM, offering high-order error bounds in law. The second part of the talk will address the design of conditions for optimality which can then be leveraged to solve general, i.e., non-smooth risk-averse SOCP through efficient numerical computations.","breadcrumb":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/on-learning-and-optimization-based-methods-for-risk-averse-control-of-autonomous-systems\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.uni.lu\/fstm-fr\/events\/on-learning-and-optimization-based-methods-for-risk-averse-control-of-autonomous-systems\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/on-learning-and-optimization-based-methods-for-risk-averse-control-of-autonomous-systems\/#primaryimage","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2023\/02\/on_learning_and_optimization_based_methods_for_risk_averse_control_of_autonomous_systems.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2023\/02\/on_learning_and_optimization_based_methods_for_risk_averse_control_of_autonomous_systems.jpg","width":800,"height":600},{"@type":"BreadcrumbList","@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/on-learning-and-optimization-based-methods-for-risk-averse-control-of-autonomous-systems\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.uni.lu\/fr"},{"@type":"ListItem","position":2,"name":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine","item":"https:\/\/www.uni.lu\/fstm-fr\/"},{"@type":"ListItem","position":3,"name":"Events","item":"https:\/\/www.uni.lu\/fstm-fr\/events\/"},{"@type":"ListItem","position":4,"name":"On Learning- and Optimization-based Methods for Risk-Averse Control of Autonomous Systems"}]},{"@type":"WebSite","@id":"https:\/\/www.uni.lu\/fstm-fr\/#website","url":"https:\/\/www.uni.lu\/fstm-fr\/","name":"FSTM","description":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine I Uni.lu","publisher":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/#organization"},"alternateName":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine I Universit\u00e9 du Luxembourg","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.uni.lu\/fstm-fr\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/www.uni.lu\/fstm-fr\/#organization","name":"FSTM - Universit\u00e9 du Luxembourg I Uni.lu","alternateName":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine","url":"https:\/\/www.uni.lu\/fstm-fr\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fstm-fr\/#\/schema\/logo\/image\/","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg","width":2560,"height":2560,"caption":"FSTM - Universit\u00e9 du Luxembourg I Uni.lu"},"image":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/fstm.uni.lu\/","https:\/\/www.linkedin.com\/showcase\/fstm-uni-lu"]}]}},"_links":{"self":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events\/2213"}],"collection":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events"}],"about":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/types\/events"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/comments?post=2213"}],"version-history":[{"count":0,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events\/2213\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/media\/2214"}],"wp:attachment":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/media?parent=2213"}],"wp:term":[{"taxonomy":"events-topic","embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events-topic?post=2213"},{"taxonomy":"events-type","embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events-type?post=2213"},{"taxonomy":"organisation","embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/organisation?post=2213"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}