{"id":"https://openalex.org/W7140282847","doi":"https://doi.org/10.48550/arxiv.2603.22527","title":"Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion","display_name":"Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140282847","doi":"https://doi.org/10.48550/arxiv.2603.22527"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.22527","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22527","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.2603.22527","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130560143","display_name":"Honglin He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Honglin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130605554","display_name":"Yukai Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Yukai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130593408","display_name":"Brad Squicciarini","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Squicciarini, Brad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000896822","display_name":"Wayne Wu","orcid":"https://orcid.org/0000-0002-1364-8151"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Wayne","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130616558","display_name":"Bolei Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Bolei","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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.17270000278949738,"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":0.17270000278949738,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.15330000221729279,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.1242000013589859,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6121000051498413},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.548799991607666},{"id":"https://openalex.org/keywords/corrective-feedback","display_name":"Corrective feedback","score":0.513700008392334},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5045999884605408},{"id":"https://openalex.org/keywords/autopilot","display_name":"Autopilot","score":0.4909000098705292},{"id":"https://openalex.org/keywords/teleoperation","display_name":"Teleoperation","score":0.47110000252723694},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.41600000858306885},{"id":"https://openalex.org/keywords/crowds","display_name":"Crowds","score":0.4004000127315521}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6121000051498413},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5934000015258789},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.548799991607666},{"id":"https://openalex.org/C2779305910","wikidata":"https://www.wikidata.org/wiki/Q5172809","display_name":"Corrective feedback","level":2,"score":0.513700008392334},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5011000037193298},{"id":"https://openalex.org/C18020424","wikidata":"https://www.wikidata.org/wiki/Q220858","display_name":"Autopilot","level":2,"score":0.4909000098705292},{"id":"https://openalex.org/C161759796","wikidata":"https://www.wikidata.org/wiki/Q3982902","display_name":"Teleoperation","level":3,"score":0.47110000252723694},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4699999988079071},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.41600000858306885},{"id":"https://openalex.org/C2777852691","wikidata":"https://www.wikidata.org/wiki/Q13430821","display_name":"Crowds","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.39259999990463257},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.30869999527931213},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2987000048160553},{"id":"https://openalex.org/C117035363","wikidata":"https://www.wikidata.org/wiki/Q3769299","display_name":"Human behavior","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C17500928","wikidata":"https://www.wikidata.org/wiki/Q959968","display_name":"Control system","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C196467688","wikidata":"https://www.wikidata.org/wiki/Q1851985","display_name":"Telerobotics","level":4,"score":0.2678999900817871},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.22527","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22527","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.2603.22527","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22527","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":[{"score":0.7669514417648315,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sidewalk":[0],"micromobility":[1],"is":[2],"a":[3,50,96],"promising":[4],"solution":[5],"for":[6],"last-mile":[7],"transportation,":[8],"but":[9],"current":[10],"learning-based":[11],"control":[12],"methods":[13],"struggle":[14],"in":[15,128],"complex":[16],"urban":[17],"environments.":[18],"Imitation":[19],"learning":[20],"(IL)":[21],"learns":[22],"policies":[23],"from":[24,86],"human":[25],"demonstrations,":[26],"yet":[27],"its":[28,87],"reliance":[29],"on":[30],"fixed":[31],"offline":[32],"data":[33,65],"often":[34],"leads":[35],"to":[36,78,82,84],"compounding":[37],"errors,":[38],"limited":[39],"robustness,":[40],"and":[41,59,75,106,114,126],"poor":[42],"generalization.":[43],"To":[44],"address":[45],"these":[46],"challenges,":[47],"we":[48,67,94],"propose":[49],"framework":[51],"that":[52,100,120],"advances":[53],"IL":[54,98],"through":[55],"corrective":[56,73],"behavior":[57],"expansion":[58],"multi-scale":[60,97],"imitation":[61],"learning.":[62],"On":[63,90],"the":[64,80,91],"side,":[66,93],"augment":[68],"teleoperation":[69],"datasets":[70],"with":[71],"diverse":[72,129],"behaviors":[74,105],"sensor":[76],"augmentations":[77],"enable":[79],"policy":[81],"learn":[83],"recover":[85],"own":[88],"mistakes.":[89],"model":[92],"introduce":[95],"architecture":[99],"captures":[101],"both":[102],"short-horizon":[103],"interactive":[104],"long-horizon":[107],"goal-directed":[108],"intentions":[109],"via":[110],"horizon-based":[111],"trajectory":[112],"clustering":[113],"hierarchical":[115],"supervision.":[116],"Real-world":[117],"experiments":[118],"show":[119],"our":[121],"approach":[122],"significantly":[123],"improves":[124],"robustness":[125],"generalization":[127],"sidewalk":[130],"scenarios.":[131]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-26T00:00:00"}
