{"id":"https://openalex.org/W2968806961","doi":"https://doi.org/10.1109/icra.2019.8794025","title":"Uncertainty-Aware Data Aggregation for Deep Imitation Learning","display_name":"Uncertainty-Aware Data Aggregation for Deep Imitation Learning","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2968806961","doi":"https://doi.org/10.1109/icra.2019.8794025","mag":"2968806961"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2019.8794025","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2019.8794025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Robotics and Automation (ICRA)","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/A5070110088","display_name":"Yuchen Cui","orcid":"https://orcid.org/0000-0001-7417-1222"},"institutions":[{"id":"https://openalex.org/I4210145184","display_name":"Honda (United States)","ror":"https://ror.org/04vdmc602","country_code":"US","type":"company","lineage":["https://openalex.org/I1283473643","https://openalex.org/I4210145184"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuchen Cui","raw_affiliation_strings":["Honda Research Institute USA, CA, USA"],"affiliations":[{"raw_affiliation_string":"Honda Research Institute USA, CA, USA","institution_ids":["https://openalex.org/I4210145184"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063634505","display_name":"David Isele","orcid":"https://orcid.org/0000-0001-9749-6951"},"institutions":[{"id":"https://openalex.org/I4210145184","display_name":"Honda (United States)","ror":"https://ror.org/04vdmc602","country_code":"US","type":"company","lineage":["https://openalex.org/I1283473643","https://openalex.org/I4210145184"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Isele","raw_affiliation_strings":["Honda Research Institute USA, CA, USA"],"affiliations":[{"raw_affiliation_string":"Honda Research Institute USA, CA, USA","institution_ids":["https://openalex.org/I4210145184"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043572737","display_name":"Scott Niekum","orcid":null},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Scott Niekum","raw_affiliation_strings":["University of Texas at Austin, Austin, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112249344","display_name":"Kikuo Fujimura","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145184","display_name":"Honda (United States)","ror":"https://ror.org/04vdmc602","country_code":"US","type":"company","lineage":["https://openalex.org/I1283473643","https://openalex.org/I4210145184"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kikuo Fujimura","raw_affiliation_strings":["Honda Research Institute USA, CA, USA"],"affiliations":[{"raw_affiliation_string":"Honda Research Institute USA, CA, USA","institution_ids":["https://openalex.org/I4210145184"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5070110088"],"corresponding_institution_ids":["https://openalex.org/I4210145184"],"apc_list":null,"apc_paid":null,"fwci":1.9602,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.89774946,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"761","last_page":"767"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9987000226974487,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/computer-science","display_name":"Computer science","score":0.784421443939209},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7091898918151855},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.6633589267730713},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.5141525864601135},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4873543679714203},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47906407713890076},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.47764885425567627},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4704412817955017},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.45291846990585327},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4246615171432495},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.41568344831466675},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.41313767433166504},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3535367250442505},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10550299286842346}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.784421443939209},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7091898918151855},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.6633589267730713},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.5141525864601135},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4873543679714203},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47906407713890076},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.47764885425567627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4704412817955017},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.45291846990585327},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4246615171432495},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.41568344831466675},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.41313767433166504},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3535367250442505},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10550299286842346},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2019.8794025","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2019.8794025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6800000071525574}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W601603264","https://openalex.org/W1534477342","https://openalex.org/W1579853615","https://openalex.org/W1731081199","https://openalex.org/W1931877416","https://openalex.org/W2095705004","https://openalex.org/W2103328396","https://openalex.org/W2133233905","https://openalex.org/W2135354436","https://openalex.org/W2142641780","https://openalex.org/W2154018708","https://openalex.org/W2167224731","https://openalex.org/W2280163991","https://openalex.org/W2342840547","https://openalex.org/W2396217537","https://openalex.org/W2538244214","https://openalex.org/W2597787948","https://openalex.org/W2600383743","https://openalex.org/W2610818722","https://openalex.org/W2611430843","https://openalex.org/W2766447205","https://openalex.org/W2767221003","https://openalex.org/W2794683864","https://openalex.org/W2951786554","https://openalex.org/W2962867954","https://openalex.org/W2962894046","https://openalex.org/W2962957031","https://openalex.org/W2963238274","https://openalex.org/W2963709863","https://openalex.org/W2963938771","https://openalex.org/W2964045208","https://openalex.org/W2964161785","https://openalex.org/W3093419064","https://openalex.org/W4295719664","https://openalex.org/W4300126339","https://openalex.org/W4300172425","https://openalex.org/W4300996741","https://openalex.org/W6618476874","https://openalex.org/W6637618735","https://openalex.org/W6640174482","https://openalex.org/W6674330103","https://openalex.org/W6675415620","https://openalex.org/W6679671054","https://openalex.org/W6679847170","https://openalex.org/W6680724558","https://openalex.org/W6684338915","https://openalex.org/W6695011786","https://openalex.org/W6704559304","https://openalex.org/W6712453370","https://openalex.org/W6730042731","https://openalex.org/W6730161283","https://openalex.org/W6735374517","https://openalex.org/W6735443497","https://openalex.org/W6737454590","https://openalex.org/W6745193792","https://openalex.org/W6745703818","https://openalex.org/W6745935785","https://openalex.org/W6749499340"],"related_works":["https://openalex.org/W3183948672","https://openalex.org/W3173606202","https://openalex.org/W3110381201","https://openalex.org/W2778153218","https://openalex.org/W1531601525","https://openalex.org/W3109499659","https://openalex.org/W4288388567","https://openalex.org/W4382315317","https://openalex.org/W2966641257","https://openalex.org/W4399530423"],"abstract_inverted_index":{"Estimating":[0],"statistical":[1],"uncertainties":[2],"allows":[3],"autonomous":[4,23],"agents":[5],"to":[6,49,65,73,82,97,102,127],"communicate":[7],"their":[8],"confidence":[9],"during":[10],"task":[11],"execution":[12],"and":[13,94],"is":[14,63],"important":[15],"for":[16,36],"applications":[17],"in":[18,52,100],"safety-critical":[19],"domains":[20],"such":[21],"as":[22],"driving.":[24],"In":[25,71],"this":[26],"work,":[27],"we":[28],"present":[29],"the":[30,53,98],"uncertainty-aware":[31],"imitation":[32],"learning":[33],"(UAIL)":[34],"algorithm":[35],"improving":[37],"end-to-end":[38,57],"control":[39,54,96],"systems":[40],"via":[41],"data":[42,75,138],"aggregation.":[43],"UAIL":[44,88,135],"applies":[45],"Monte":[46],"Carlo":[47],"Dropout":[48],"estimate":[50],"uncertainty":[51,121],"output":[55],"of":[56,107,144],"systems,":[58],"using":[59],"states":[60,85],"where":[61],"it":[62],"uncertain":[64],"selectively":[66],"acquire":[67],"new":[68],"training":[69],"data.":[70],"contrast":[72],"prior":[74],"aggregation":[76,139],"algorithms":[77,140],"that":[78,118,134],"force":[79],"human":[80],"experts":[81],"visit":[83],"sub-optimal":[84,108],"at":[86],"random,":[87],"can":[89,124],"anticipate":[90],"its":[91],"own":[92],"mistakes":[93],"switch":[95],"expert":[99],"order":[101],"prevent":[103],"visiting":[104],"a":[105,142],"series":[106,143],"states.":[109],"Our":[110,131],"experimental":[111],"results":[112],"from":[113],"simulated":[114],"driving":[115],"tasks":[116],"demonstrate":[117],"our":[119],"proposed":[120],"estimation":[122],"method":[123],"be":[125],"leveraged":[126],"reliably":[128],"predict":[129],"infractions.":[130],"analysis":[132],"shows":[133],"outperforms":[136],"existing":[137],"on":[141],"benchmark":[145],"tasks.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}