{"id":"https://openalex.org/W7154474997","doi":"https://doi.org/10.48550/arxiv.2604.12542","title":"Goal-oriented safe active learning for predictive control using Bayesian recurrent neural networks","display_name":"Goal-oriented safe active learning for predictive control using Bayesian recurrent neural networks","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154474997","doi":"https://doi.org/10.48550/arxiv.2604.12542"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12542","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12542","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.12542","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093123121","display_name":"Laura Boca de Giuli","orcid":"https://orcid.org/0009-0005-8943-233X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de Giuli, Laura Boca","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041819485","display_name":"Alessio La Bella","orcid":"https://orcid.org/0000-0001-6359-1694"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"La Bella, Alessio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106374192","display_name":"Manish Prajapat","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prajapat, Manish","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133691563","display_name":"Johannes K\u00f6hler","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"K\u00f6hler, Johannes","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010859011","display_name":"Anna Scampicchio","orcid":"https://orcid.org/0000-0002-2238-6341"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Scampicchio, Anna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010643637","display_name":"Riccardo Scattolini","orcid":"https://orcid.org/0000-0003-1804-9921"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Scattolini, Riccardo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133632369","display_name":"Melanie Zeilinger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeilinger, Melanie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10791","display_name":"Advanced Control Systems Optimization","score":0.6654999852180481,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10791","display_name":"Advanced Control Systems Optimization","score":0.6654999852180481,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.17350000143051147,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.05570000037550926,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/model-predictive-control","display_name":"Model predictive control","score":0.6934999823570251},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6585000157356262},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6498000025749207},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6018999814987183},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.510699987411499},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4885999858379364},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.47279998660087585},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.4578000009059906},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3970000147819519}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7335000038146973},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.6934999823570251},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6585000157356262},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6498000025749207},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6194999814033508},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6018999814987183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5813000202178955},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.510699987411499},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4885999858379364},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.47279998660087585},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4578000009059906},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3970000147819519},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.3953999876976013},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3880999982357025},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3499000072479248},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C17500928","wikidata":"https://www.wikidata.org/wiki/Q959968","display_name":"Control system","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C40343088","wikidata":"https://www.wikidata.org/wiki/Q3059012","display_name":"Recursive Bayesian estimation","level":3,"score":0.25690001249313354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12542","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12542","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.12542","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12542","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.510171115398407,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"A":[0],"key":[1],"challenge":[2],"in":[3,41,126],"learning-based":[4],"model":[5,16,33,87,161],"predictive":[6],"control":[7,24,94,107],"(MPC)":[8],"is":[9,58,111],"to":[10,82,149],"collect":[11,83],"informative":[12,84],"data":[13,85],"online":[14,32],"for":[15,86],"adaptation":[17,34,88],"while":[18,89,158],"ensuring":[19],"safety":[20,166],"and":[21,96,129,163],"without":[22],"penalising":[23],"performance.":[25,132],"In":[26],"this":[27],"paper,":[28],"we":[29],"propose":[30],"an":[31,38,72,152],"scheme":[35],"embedded":[36],"within":[37],"MPC":[39,77,153],"framework":[40,144],"which":[42],"the":[43,76,92,105,142],"last-layer":[44],"parameters":[45],"of":[46,62,116,124,151],"a":[47,63,97,136],"recurrent":[48],"neural":[49],"network":[50],"are":[51],"recursively":[52],"updated":[53],"via":[54],"Bayesian":[55],"learning.":[56],"This":[57],"achieved":[59],"by":[60],"means":[61],"goal-oriented":[64],"safe":[65],"active":[66],"learning":[67],"algorithm":[68,110],"that":[69,141,150],"alternates":[70],"between":[71],"exploration":[73,125],"phase,":[74,99],"where":[75,100],"actively":[78],"explores":[79],"system":[80,139,156],"dynamics":[81],"still":[90],"pursuing":[91],"main":[93,106],"objective,":[95],"goal-reaching":[98],"it":[101],"focuses":[102],"exclusively":[103],"on":[104,135],"objective.":[108],"The":[109],"complemented":[112],"with":[113,154,168],"theoretical":[114],"guarantees":[115],"(i)":[117],"recursive":[118],"feasibility,":[119],"(ii)":[120],"safety,":[121],"(iii)":[122],"termination":[123],"finite":[127],"time,":[128],"(iv)":[130],"close-to-optimal":[131],"Simulation":[133],"results":[134],"benchmark":[137],"energy":[138],"demonstrate":[140],"proposed":[143],"achieves":[145],"economic":[146],"performance":[147],"comparable":[148],"full":[155],"knowledge,":[157],"progressively":[159],"improving":[160],"accuracy":[162],"respecting":[164],"operational":[165],"constraints":[167],"high":[169],"probability.":[170]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-16T00:00:00"}