{"id":"https://openalex.org/W2739329003","doi":"https://doi.org/10.1109/icra.2017.7989113","title":"Apprenticeship learning in an incompatible feature space","display_name":"Apprenticeship learning in an incompatible feature space","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2739329003","doi":"https://doi.org/10.1109/icra.2017.7989113","mag":"2739329003"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2017.7989113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2017.7989113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 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/A5046607392","display_name":"Gakuto Masuyama","orcid":"https://orcid.org/0000-0002-4776-4863"},"institutions":[{"id":"https://openalex.org/I96679780","display_name":"Chuo University","ror":"https://ror.org/03qvqb743","country_code":"JP","type":"education","lineage":["https://openalex.org/I96679780"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Gakuto Masuyama","raw_affiliation_strings":["Department of Precision Mechanics, Chuo University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Precision Mechanics, Chuo University, Tokyo, Japan","institution_ids":["https://openalex.org/I96679780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083854260","display_name":"Kazunori Umeda","orcid":"https://orcid.org/0000-0002-4458-4648"},"institutions":[{"id":"https://openalex.org/I96679780","display_name":"Chuo University","ror":"https://ror.org/03qvqb743","country_code":"JP","type":"education","lineage":["https://openalex.org/I96679780"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazunori Umeda","raw_affiliation_strings":["Department of Precision Mechanics, Chuo University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Precision Mechanics, Chuo University, Tokyo, Japan","institution_ids":["https://openalex.org/I96679780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5046607392"],"corresponding_institution_ids":["https://openalex.org/I96679780"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09172401,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"94","issue":null,"first_page":"932","last_page":"938"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9955000281333923,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9955000281333923,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9684000015258789,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9513000249862671,"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/feature","display_name":"Feature (linguistics)","score":0.753753662109375},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6773698329925537},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.6231157779693604},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.5856112837791443},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5666211843490601},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5242248177528381},{"id":"https://openalex.org/keywords/apprenticeship","display_name":"Apprenticeship","score":0.5229621529579163},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.5161317586898804},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5059031844139099},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4387255609035492},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4223371148109436},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22130483388900757},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08477449417114258}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.753753662109375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6773698329925537},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.6231157779693604},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.5856112837791443},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5666211843490601},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5242248177528381},{"id":"https://openalex.org/C107806365","wikidata":"https://www.wikidata.org/wiki/Q253567","display_name":"Apprenticeship","level":2,"score":0.5229621529579163},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.5161317586898804},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5059031844139099},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4387255609035492},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4223371148109436},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22130483388900757},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08477449417114258},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2017.7989113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2017.7989113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W286998138","https://openalex.org/W1591675293","https://openalex.org/W1925816294","https://openalex.org/W1972336342","https://openalex.org/W1977655452","https://openalex.org/W1999874108","https://openalex.org/W2034852725","https://openalex.org/W2061562262","https://openalex.org/W2084461292","https://openalex.org/W2088042731","https://openalex.org/W2101786389","https://openalex.org/W2108689239","https://openalex.org/W2113023245","https://openalex.org/W2133068870","https://openalex.org/W2156163138","https://openalex.org/W2156940638","https://openalex.org/W2158148009","https://openalex.org/W2165150801","https://openalex.org/W2165698076","https://openalex.org/W2169498096","https://openalex.org/W2172968643","https://openalex.org/W2258149283","https://openalex.org/W2566991354","https://openalex.org/W3005581722","https://openalex.org/W4302570325","https://openalex.org/W6635261211","https://openalex.org/W6640290305","https://openalex.org/W6676728370","https://openalex.org/W6679958247","https://openalex.org/W6682974852","https://openalex.org/W6683119562","https://openalex.org/W6684205842"],"related_works":["https://openalex.org/W2130264791","https://openalex.org/W4235622043","https://openalex.org/W2155887593","https://openalex.org/W2102428166","https://openalex.org/W1600547024","https://openalex.org/W3125491562","https://openalex.org/W2315999538","https://openalex.org/W187740018","https://openalex.org/W2162286586","https://openalex.org/W4255368532"],"abstract_inverted_index":{"This":[0],"study":[1],"presents":[2],"a":[3,10,28,37],"novel":[4],"apprenticeship":[5,88],"learning":[6],"method":[7,69,92,98,118],"to":[8,12,42,60,76,80,99,111,128],"enable":[9],"learner":[11,29],"utilize":[13],"demonstrations":[14,48],"observed":[15],"in":[16,49,65,119,122],"an":[17,25,50,103],"incompatible":[18],"feature":[19,44,63,105],"space.":[20,52,106],"It":[21],"is":[22,40,58,70,74,109,125],"assumed":[23],"that":[24],"expert":[26,135],"and":[27,36,136],"follow":[30],"non-identical":[31],"Markov":[32],"decision":[33],"processes":[34,79],"(MDPs),":[35],"mapping":[38],"function":[39],"estimated":[41],"obtain":[43],"expectation":[45,64],"of":[46,84,115,133],"the":[47,62,91,113,116,131,134],"agent":[51,104],"A":[53,107],"conditional":[54],"density":[55],"estimation":[56],"technique":[57],"used":[59,110],"represent":[61],"closed-form.":[66],"The":[67],"proposed":[68,117],"useful":[71],"because":[72],"it":[73,124],"expected":[75],"alleviate":[77],"intractable":[78],"explicitly":[81],"specify":[82],"correspondence":[83],"heterogeneous":[85],"MDPs":[86],"for":[87],"learning.":[89],"Additionally,":[90],"does":[93],"not":[94,126],"require":[95],"any":[96],"sampling":[97],"approximate":[100],"integrals":[101],"over":[102],"simulation":[108],"demonstrate":[112],"validity":[114],"three":[120],"domains":[121],"which":[123],"possible":[127],"directly":[129],"compare":[130],"features":[132],"learner.":[137]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
