{"id":"https://openalex.org/W7160932339","doi":"https://doi.org/10.48550/arxiv.2605.08713","title":"REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer","display_name":"REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160932339","doi":"https://doi.org/10.48550/arxiv.2605.08713"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.08713","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08713","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.2605.08713","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052605358","display_name":"Changze Li","orcid":"https://orcid.org/0000-0003-3004-1818"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Changze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135921289","display_name":"Zhe Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135939170","display_name":"Shaoyu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Shaoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067063678","display_name":"Lisen Mu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mu, Lisen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135987288","display_name":"Yijian Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yijian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135985092","display_name":"Yuelong Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yuelong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136000543","display_name":"Qian Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135928755","display_name":"Qing Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135980401","display_name":"Ming Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135988993","display_name":"Tong Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Tong","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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.4607999920845032,"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"}},"topics":[{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.4607999920845032,"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":0.210999995470047,"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"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.14239999651908875,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7721999883651733},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.4562000036239624},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.3894999921321869},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.37450000643730164},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.3637999892234802},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.3587000072002411},{"id":"https://openalex.org/keywords/parking-lot","display_name":"Parking lot","score":0.3125},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.3082999885082245}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8130000233650208},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7721999883651733},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.4562000036239624},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3887999951839447},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.36890000104904175},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3637999892234802},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3580999970436096},{"id":"https://openalex.org/C2777427512","wikidata":"https://www.wikidata.org/wiki/Q6501349","display_name":"Parking lot","level":2,"score":0.3125},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.28110000491142273},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2637999951839447},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C60692881","wikidata":"https://www.wikidata.org/wiki/Q584529","display_name":"Humanoid robot","level":3,"score":0.2513999938964844},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.08713","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08713","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.2605.08713","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08713","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":0.5029760003089905,"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":{"In":[0,189,206],"recent":[1],"years,":[2],"autonomous":[3],"parking":[4,10,23,35,47,77,240,249],"has":[5],"made":[6],"significant":[7],"advances,":[8],"yet":[9],"tasks":[11],"still":[12],"face":[13],"challenges":[14],"in":[15,27,71,224,236,250],"extreme":[16,72],"scenarios":[17],"such":[18],"as":[19,49],"mechanical":[20,253],"and":[21,59,91,130,228],"dead-end":[22],"slots,":[24],"often":[25],"resulting":[26],"failures.":[28],"This":[29],"is":[30,86],"mainly":[31],"due":[32],"to":[33,44,62,126,161,180,199,218],"traditional":[34],"methods":[36,53,78],"adopting":[37],"a":[38,50,107,142,155,186],"multistage":[39],"approach,":[40],"lacking":[41],"the":[42,46,64,84,95,139,146,172,190,207,212,216,220,225,244],"ability":[43],"optimize":[45],"problem":[48],"whole.":[51],"End-to-end":[52,110],"enable":[54],"joint":[55],"optimization":[56],"across":[57],"perception":[58],"planning":[60],"modules":[61],"eliminate":[63],"accumulation":[65],"of":[66,141,239,246],"errors,":[67],"enhancing":[68],"algorithm":[69],"performance":[70],"scenarios.":[73],"Although":[74],"several":[75],"end-to-end":[76,147,213,247],"use":[79,194],"imitation":[80],"or":[81],"reinforcement":[82,123,174],"learning,":[83],"former":[85],"limited":[87],"by":[88,165],"data":[89],"cost":[90],"distribution":[92],"coverage,":[93],"while":[94],"latter":[96],"suffers":[97],"from":[98],"inefficient":[99],"exploration.":[100],"To":[101,133,169],"address":[102],"these":[103],"challenges,":[104],"we":[105,137,183,193,210],"propose":[106],"Reinforcement":[108],"learning":[109,124,175],"Autonomous":[111],"Parking":[112],"method":[113],"(REAP).":[114],"REAP":[115,233],"employs":[116],"Soft":[117],"Actor-Critic":[118],"(SAC)":[119],"within":[120],"an":[121],"asymmetric":[122],"framework":[125],"improve":[127],"training":[128],"efficiency":[129],"inference":[131],"performance.":[132],"accelerate":[134],"model":[135,214],"convergence,":[136],"distill":[138],"capabilities":[140],"rule-based":[143],"planner":[144],"into":[145,203],"network":[148,176],"through":[149],"behavior":[150],"cloning.":[151],"We":[152],"further":[153],"introduce":[154],"soft":[156],"predictive":[157],"collision":[158,163],"penalty":[159],"mechanism":[160],"reduce":[162],"rates":[164],"penalizing":[166],"obstacle-approaching":[167],"actions.":[168],"ensure":[170],"that":[171],"trained":[173],"can":[177],"directly":[178],"transfer":[179],"real-world":[181,201],"scenarios,":[182],"have":[184],"established":[185],"Real2Sim2Real":[187],"simulator.":[188],"Real2Sim":[191],"step,":[192,209],"3D":[195],"Gaussian":[196],"Splatting":[197],"(3DGS)":[198],"transform":[200],"scenes":[202],"digital":[204],"scenes.":[205],"Sim2Real":[208,221],"deploy":[211],"onto":[215],"vehicle":[217],"bridge":[219],"gap.":[222],"Trained":[223],"3DGS":[226],"simulator":[227],"deployed":[229],"on":[230],"physical":[231],"vehicles,":[232],"successfully":[234],"parks":[235],"various":[237],"types":[238],"spaces,":[241],"especially":[242],"demonstrating":[243],"feasibility":[245],"RL":[248],"extremely":[251],"narrow":[252],"slots.":[254]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-13T00:00:00"}
