{"id":"https://openalex.org/W7164935652","doi":"https://doi.org/10.48550/arxiv.2606.16888","title":"LOPAL: Local Performance-Aware Active Learning from Imperfect Demonstrations","display_name":"LOPAL: Local Performance-Aware Active Learning from Imperfect Demonstrations","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164935652","doi":"https://doi.org/10.48550/arxiv.2606.16888"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.16888","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16888","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.16888","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052579496","display_name":"Johannes Heidersberger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heidersberger, Johannes","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137389067","display_name":"Shail Jadav","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jadav, Shail","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5033836068","display_name":"D Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Dongheui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.8355000019073486,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.8355000019073486,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.042100001126527786,"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":0.02160000056028366,"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/task","display_name":"Task (project management)","score":0.7260000109672546},{"id":"https://openalex.org/keywords/imperfect","display_name":"Imperfect","score":0.6654000282287598},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5972999930381775},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5436999797821045},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.49630001187324524},{"id":"https://openalex.org/keywords/human\u2013robot-interaction","display_name":"Human\u2013robot interaction","score":0.4343999922275543},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4293999969959259},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.4020000100135803}],"concepts":[{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7260000109672546},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.70660001039505},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.6654000282287598},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5972999930381775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.59579998254776},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5436999797821045},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.492000013589859},{"id":"https://openalex.org/C145460709","wikidata":"https://www.wikidata.org/wiki/Q859951","display_name":"Human\u2013robot interaction","level":3,"score":0.4343999922275543},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4293999969959259},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.4020000100135803},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.3569999933242798},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.3167000114917755},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.303600013256073},{"id":"https://openalex.org/C188888258","wikidata":"https://www.wikidata.org/wiki/Q7353390","display_name":"Robot learning","level":4,"score":0.30079999566078186},{"id":"https://openalex.org/C132758656","wikidata":"https://www.wikidata.org/wiki/Q5307365","display_name":"Dreyfus model of skill acquisition","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C193611912","wikidata":"https://www.wikidata.org/wiki/Q4677596","display_name":"Active vision","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C74222875","wikidata":"https://www.wikidata.org/wiki/Q16000312","display_name":"Robot kinematics","level":4,"score":0.2858000099658966},{"id":"https://openalex.org/C2779038628","wikidata":"https://www.wikidata.org/wiki/Q7248497","display_name":"Programming by demonstration","level":3,"score":0.28349998593330383},{"id":"https://openalex.org/C2776010242","wikidata":"https://www.wikidata.org/wiki/Q4677575","display_name":"Active perception","level":3,"score":0.2655999958515167},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.257999986410141}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.16888","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16888","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.16888","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16888","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6022335886955261}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Learning":[0],"from":[1,15,174],"Demonstration":[2],"(LfD)":[3],"enables":[4,97],"intuitive":[5],"robot":[6,152],"skill":[7],"acquisition":[8,118],"by":[9,107,126],"allowing":[10],"robots":[11],"to":[12,24,30,84,120,141,188,201],"learn":[13],"directly":[14],"human":[16,34],"task":[17,179,193],"demonstrations.":[18,204],"However,":[19],"current":[20],"methods":[21],"often":[22],"fail":[23],"address":[25],"the":[26,36,39,87,98,104,123,136,151,155,182,198,203],"fact":[27],"that":[28,58,102,181],"due":[29],"suboptimal":[31],"and":[32,90,168],"inconsistent":[33],"behavior,":[35],"quality":[37,63,94],"of":[38,68,100,112,160],"demonstration":[40,62],"can":[41,185],"vary":[42],"within":[43],"each":[44],"demonstration.":[45],"Therefore,":[46],"we":[47],"introduce":[48],"LOPAL":[49,161],"(LOcal":[50],"Performance-aware":[51],"Active":[52],"Learning),":[53],"an":[54],"active":[55,116],"learning":[56],"approach":[57,66,184],"leverages":[59],"this":[60],"local":[61,74,93,110],"information.":[64],"Our":[65],"consists":[67],"two":[69],"synergistic":[70],"components.":[71],"First,":[72],"a":[73,79,145,166,169,175],"performance-driven":[75],"LfD":[76],"method":[77],"uses":[78],"Gaussian":[80],"Mixture":[81],"Model":[82],"(GMM)":[83],"encode":[85],"both":[86,165],"demonstrated":[88],"trajectories":[89,101],"their":[91],"associated":[92],"assessments.":[95],"This":[96],"generation":[99],"outperform":[103],"imperfect":[105,124],"demonstrations":[106,125],"utilizing":[108],"complementary":[109],"data":[111,117],"high":[113],"performance.":[114],"Second,":[115],"allows":[119],"improve":[121],"beyond":[122],"collecting":[127],"additional":[128],"informative":[129],"samples.":[130],"In":[131],"areas":[132],"missing":[133],"good":[134],"data,":[135],"user":[137],"is":[138],"actively":[139],"requested":[140],"provide":[142],"corrections":[143],"through":[144],"shared":[146],"autonomy":[147],"(SA)":[148],"mechanism,":[149],"while":[150,195],"autonomously":[153],"executes":[154],"learned":[156],"behavior.":[157],"The":[158,172],"efficacy":[159],"was":[162],"validated":[163],"in":[164,192],"simulation":[167],"real-world":[170,176],"experiment.":[171],"results":[173],"pipe":[177],"inspection":[178],"showed":[180],"proposed":[183],"achieve":[186],"up":[187],"27.31":[189],"%":[190],"improvement":[191],"performance":[194],"also":[196],"reducing":[197],"effort":[199],"required":[200],"collect":[202]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-17T00:00:00"}