{"id":"https://openalex.org/W2789079512","doi":"https://doi.org/10.1109/icra.2018.8460735","title":"Learning to Race Through Coordinate Descent Bayesian Optimisation","display_name":"Learning to Race Through Coordinate Descent Bayesian Optimisation","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2789079512","doi":"https://doi.org/10.1109/icra.2018.8460735","mag":"2789079512"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2018.8460735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8460735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1802.06179","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Rafael Oliveira","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 Oliveira","raw_affiliation_strings":["School of Information Technologies, The University of Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Technologies, The University of Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fernando H. M. Rocha","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":"Fernando H. M. Rocha","raw_affiliation_strings":["School of Information Technologies, The University of Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Technologies, The University of Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lionel Ott","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":"Lionel Ott","raw_affiliation_strings":["School of Information Technologies, The University of Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Technologies, The University of Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Vitor Guizilini","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":"Vitor Guizilini","raw_affiliation_strings":["School of Information Technologies, The University of Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Technologies, The University of Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fabio Ramos","orcid":null},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Fabio Ramos","raw_affiliation_strings":["Sao Carlos School of Engineering at the University of Sao Paulo, Sao Carlos, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"Sao Carlos School of Engineering at the University of Sao Paulo, Sao Carlos, SP, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"last","author":{"id":null,"display_name":"Valdir Grassi","orcid":null},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Valdir Grassi","raw_affiliation_strings":["Sao Carlos School of Engineering at the University of Sao Paulo, Sao Carlos, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"Sao Carlos School of Engineering at the University of Sao Paulo, Sao Carlos, SP, Brazil","institution_ids":["https://openalex.org/I17974374"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I129604602"],"apc_list":null,"apc_paid":null,"fwci":0.3385,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.66741338,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"6431","last_page":"6438"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9973999857902527,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9973999857902527,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.9968000054359436,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5680000185966492},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.527899980545044},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.5249000191688538},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.5145999789237976},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5011000037193298},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.47690001130104065},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4749999940395355},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4657000005245209},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.4625999927520752},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.446399986743927}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5680000185966492},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5602999925613403},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.527899980545044},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.5249000191688538},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.5145999789237976},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5011000037193298},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.47690001130104065},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4749999940395355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46650001406669617},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4657000005245209},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.4625999927520752},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.446399986743927},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4187000095844269},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35359999537467957},{"id":"https://openalex.org/C157553263","wikidata":"https://www.wikidata.org/wiki/Q5168004","display_name":"Coordinate descent","level":2,"score":0.3513999879360199},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C2776637919","wikidata":"https://www.wikidata.org/wiki/Q624380","display_name":"Descent (aeronautics)","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31369999051094055},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2802000045776367},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.27559998631477356},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27459999918937683},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.26170000433921814},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.2578999996185303}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icra.2018.8460735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8460735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1802.06179","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.06179","pdf_url":"https://arxiv.org/pdf/1802.06179","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1802.06179","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.06179","pdf_url":"https://arxiv.org/pdf/1802.06179","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1578293866","https://openalex.org/W1929309940","https://openalex.org/W1979089199","https://openalex.org/W2000769684","https://openalex.org/W2016178266","https://openalex.org/W2026258334","https://openalex.org/W2056525874","https://openalex.org/W2058437545","https://openalex.org/W2095984592","https://openalex.org/W2121658992","https://openalex.org/W2137294168","https://openalex.org/W2142916680","https://openalex.org/W2273233231","https://openalex.org/W2583993537","https://openalex.org/W2738778707","https://openalex.org/W2963930094","https://openalex.org/W6628937730","https://openalex.org/W6633018774","https://openalex.org/W6653548176","https://openalex.org/W6675200109","https://openalex.org/W6677614981","https://openalex.org/W6681367012","https://openalex.org/W6681672864","https://openalex.org/W6682093400","https://openalex.org/W6683037491","https://openalex.org/W6686801988","https://openalex.org/W6708241452","https://openalex.org/W6732439103","https://openalex.org/W6761030284"],"related_works":[],"abstract_inverted_index":{"In":[0,32],"the":[1,8,16,29,44,51,61,64,68,90,95,120,127,146,157,175,181,184,226,229],"automation":[2],"of":[3,6,19,23,43,97,115,153,228],"many":[4],"kinds":[5],"processes,":[7],"observable":[9],"outcome":[10],"can":[11,80],"often":[12],"be":[13,56,73,81],"described":[14],"as":[15,84,87],"combined":[17],"effect":[18],"an":[20,135,151,200],"entire":[21],"sequence":[22],"actions,":[24],"or":[25,129],"controls,":[26],"applied":[27,143],"throughout":[28],"process":[30,45],"execution.":[31],"these":[33],"cases,":[34],"strategies":[35],"to":[36,55,66,101,105,144,149,202],"optimise":[37],"control":[38,99],"policies":[39,100],"for":[40,169],"individual":[41],"stages":[42],"are":[46],"not":[47],"applicable,":[48],"and":[49,148],"instead":[50],"whole":[52],"policy":[53,172],"needs":[54],"optimised":[57],"at":[58],"once.":[59],"On":[60],"other":[62,232],"hand,":[63],"cost":[65],"evaluate":[67],"policy's":[69],"performance":[70,227],"might":[71],"also":[72],"high,":[74],"being":[75],"desirable":[76],"that":[77,119,162,173],"a":[78,103,107,112,167,171,187,193,219,235],"solution":[79],"found":[82],"with":[83,89,210],"few":[85],"interactions":[86],"possible":[88],"real":[91],"system.":[92],"We":[93,117,198],"consider":[94],"problem":[96],"optimising":[98],"allow":[102],"robot":[104,121,147],"complete":[106],"given":[108],"race":[109],"track":[110,128,185],"within":[111],"minimum":[113],"amount":[114],"time.":[116],"assume":[118],"has":[122],"no":[123],"prior":[124],"information":[125],"about":[126],"its":[130,154],"own":[131],"dynamical":[132],"model,":[133],"just":[134],"initial":[136],"valid":[137],"driving":[138],"example.":[139],"Localisation":[140],"is":[141],"only":[142],"monitor":[145],"provide":[150],"indication":[152],"position":[155],"along":[156],"track's":[158],"centre":[159],"axis.":[160],"With":[161],"in":[163,218,234],"mind,":[164],"we":[165],"propose":[166],"method":[168],"finding":[170],"minimises":[174],"time":[176],"per":[177],"lap":[178],"while":[179],"keeping":[180],"vehicle":[182],"on":[183],"using":[186],"Bayesian":[188],"optimisation":[189],"(BO)":[190],"approach":[191],"over":[192,206,214],"reproducing":[194],"kernel":[195],"Hilbert":[196],"space.":[197],"apply":[199],"algorithm":[201,230],"search":[203],"more":[204],"efficiently":[205],"high-dimensional":[207],"policy-parameter":[208],"spaces":[209],"BO,":[211],"by":[212],"iterating":[213],"each":[215],"dimension":[216],"individually,":[217],"sequential":[220],"coordinate":[221],"descent-like":[222],"scheme.":[223],"Experiments":[224],"demonstrate":[225],"against":[231],"methods":[233],"simulated":[236],"car":[237],"racing":[238],"environment.":[239]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2018-03-06T00:00:00"}
