{"id":"https://openalex.org/W3129658336","doi":"https://doi.org/10.1109/iros45743.2020.9341401","title":"Online BayesSim for Combined Simulator Parameter Inference and Policy Improvement","display_name":"Online BayesSim for Combined Simulator Parameter Inference and Policy Improvement","publication_year":2020,"publication_date":"2020-10-24","ids":{"openalex":"https://openalex.org/W3129658336","doi":"https://doi.org/10.1109/iros45743.2020.9341401","mag":"3129658336"},"language":"en","primary_location":{"id":"doi:10.1109/iros45743.2020.9341401","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros45743.2020.9341401","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006269358","display_name":"Rafael Possas","orcid":null},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Rafael Possas","raw_affiliation_strings":["NVIDIA","University of Sydney"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]},{"raw_affiliation_string":"University of Sydney","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079951750","display_name":"Lucas Barcelos","orcid":null},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Lucas Barcelos","raw_affiliation_strings":["University of Sydney"],"affiliations":[{"raw_affiliation_string":"University of Sydney","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029891035","display_name":"Rafael Oliveira","orcid":"https://orcid.org/0000-0002-3586-5026"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Rafael Oliveira","raw_affiliation_strings":["University of Sydney"],"affiliations":[{"raw_affiliation_string":"University of Sydney","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108257764","display_name":"Dieter Fox","orcid":"https://orcid.org/0009-0009-4694-9127"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dieter Fox","raw_affiliation_strings":["NVIDIA","University of Washington"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]},{"raw_affiliation_string":"University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062619542","display_name":"F\u00e1bio Ramos","orcid":"https://orcid.org/0000-0002-2996-2188"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Fabio Ramos","raw_affiliation_strings":["NVIDIA","University of Sydney"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]},{"raw_affiliation_string":"University of Sydney","institution_ids":["https://openalex.org/I129604602"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5006269358"],"corresponding_institution_ids":["https://openalex.org/I129604602"],"apc_list":null,"apc_paid":null,"fwci":0.7954,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.79314652,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5445","last_page":"5452"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11195","display_name":"Simulation Techniques and Applications","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7631688117980957},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6819021701812744},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5480905771255493},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5333214998245239},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5224389433860779},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5129649043083191},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.509084165096283},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.49395614862442017},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.4494490325450897},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4438539445400238},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.43176528811454773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4039987325668335},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3533661365509033},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13883239030838013},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12055972218513489}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7631688117980957},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6819021701812744},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5480905771255493},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5333214998245239},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5224389433860779},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5129649043083191},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.509084165096283},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.49395614862442017},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.4494490325450897},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4438539445400238},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.43176528811454773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4039987325668335},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3533661365509033},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13883239030838013},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12055972218513489},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros45743.2020.9341401","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros45743.2020.9341401","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W114923250","https://openalex.org/W1540723801","https://openalex.org/W1579853615","https://openalex.org/W1598493736","https://openalex.org/W2003132288","https://openalex.org/W2034795216","https://openalex.org/W2045973738","https://openalex.org/W2051823273","https://openalex.org/W2064675550","https://openalex.org/W2116416291","https://openalex.org/W2141559645","https://openalex.org/W2145339207","https://openalex.org/W2148853660","https://openalex.org/W2167856595","https://openalex.org/W2173248099","https://openalex.org/W2201581102","https://openalex.org/W2605102758","https://openalex.org/W2736601468","https://openalex.org/W2781726626","https://openalex.org/W2822752092","https://openalex.org/W2898436992","https://openalex.org/W2946544065","https://openalex.org/W2963238245","https://openalex.org/W2963477884","https://openalex.org/W2963822196","https://openalex.org/W2963864421","https://openalex.org/W2963906246","https://openalex.org/W2964001908","https://openalex.org/W2964250089","https://openalex.org/W2968116426","https://openalex.org/W3006585854","https://openalex.org/W3091711922","https://openalex.org/W3098057351","https://openalex.org/W3101442004","https://openalex.org/W4300799055","https://openalex.org/W6634817459","https://openalex.org/W6684921986","https://openalex.org/W6687681856","https://openalex.org/W6740801417","https://openalex.org/W6741002519","https://openalex.org/W6747473740","https://openalex.org/W6751496971","https://openalex.org/W6755561334","https://openalex.org/W6756103505","https://openalex.org/W6763108371","https://openalex.org/W6966558720"],"related_works":["https://openalex.org/W4226115828","https://openalex.org/W4286981651","https://openalex.org/W3197430630","https://openalex.org/W1529069387","https://openalex.org/W2078267893","https://openalex.org/W2111053936","https://openalex.org/W88131178","https://openalex.org/W3033856829","https://openalex.org/W1563519935","https://openalex.org/W3192946336"],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,42,65],"Bayesian":[3],"likelihood-free":[4],"inference":[5,56],"enables":[6],"a":[7,33,43,66,74,100],"probabilistic":[8],"treatment":[9],"for":[10],"the":[11,37,51,81,86,89],"problem":[12],"of":[13,22,53,77],"estimating":[14],"simulation":[15,54],"parameters":[16,41,78],"and":[17,62,79,96],"their":[18],"uncertainty":[19,39],"given":[20],"sequences":[21],"observations.":[23],"Domain":[24],"randomization":[25,120],"can":[26],"be":[27],"performed":[28],"much":[29],"more":[30],"effectively":[31],"when":[32,99,115],"posterior":[34],"distribution":[35],"provides":[36],"correct":[38],"over":[40],"simulated":[44],"environment.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49],"study":[50],"integration":[52],"parameter":[55],"with":[57],"both":[58,109],"model-free":[59],"reinforcement":[60],"learning":[61,73],"model-based":[63],"control":[64,110],"novel":[67],"sequential":[68],"algorithm":[69],"that":[70,108],"alternates":[71],"between":[72,88],"better":[75,113],"estimation":[76],"improving":[80],"controller.":[82],"This":[83],"approach":[84],"exploits":[85],"interdependence":[87],"two":[90],"problems":[91],"to":[92,117],"generate":[93],"computational":[94],"efficiencies":[95],"improved":[97],"reliability":[98],"black-box":[101],"simulator":[102],"is":[103],"available.":[104],"Experimental":[105],"results":[106],"suggest":[107],"strategies":[111],"have":[112],"performance":[114],"compared":[116],"traditional":[118],"domain":[119],"methods.":[121]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
