{"id":"https://openalex.org/W7165642634","doi":"https://doi.org/10.48550/arxiv.2606.22860","title":"HiL-ResRL: A Model-Agnostic Finetuning Adapter via Human-in-the-loop Residual Reinforcement Learning","display_name":"HiL-ResRL: A Model-Agnostic Finetuning Adapter via Human-in-the-loop Residual Reinforcement Learning","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165642634","doi":"https://doi.org/10.48550/arxiv.2606.22860"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.22860","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22860","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"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.2606.22860","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139134646","display_name":"Jingyi Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108652210","display_name":"Zhaohong Mai","orcid":"https://orcid.org/0009-0005-1391-8093"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mai, Zhaohong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136884312","display_name":"Shunsen He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, ShunSen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139158297","display_name":"Hang Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Hang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139131853","display_name":"Chao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042268476","display_name":"Shunbo Zhou","orcid":"https://orcid.org/0000-0002-6077-4951"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Shunbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090578536","display_name":"X . Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, XiaoDong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139128747","display_name":"Heng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Heng","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":0.5695000290870667,"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":0.5695000290870667,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.19220000505447388,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.11429999768733978,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7366999983787537},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.6355000138282776},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4595000147819519},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4343999922275543},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.3993000090122223},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.3855000138282776},{"id":"https://openalex.org/keywords/cloning","display_name":"Cloning (programming)","score":0.3772999942302704},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.3767000138759613}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7366999983787537},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7254999876022339},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6355000138282776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.555400013923645},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4595000147819519},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4343999922275543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41850000619888306},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3993000090122223},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3855000138282776},{"id":"https://openalex.org/C121050878","wikidata":"https://www.wikidata.org/wiki/Q5135020","display_name":"Cloning (programming)","level":2,"score":0.3772999942302704},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3619000017642975},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.30140000581741333},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.25940001010894775}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.22860","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22860","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.22860","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22860","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5200527906417847,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,42,69,132,152,189],"generative":[3],"imitation":[4,133],"learning":[5,77],"have":[6],"significantly":[7],"propelled":[8],"the":[9,15,37,62,128,169],"field":[10],"of":[11,17,39,65,100,164],"robotic":[12,154],"manipulation.":[13],"However,":[14],"majority":[16],"existing":[18],"models":[19,41,68,188],"rely":[20],"heavily":[21],"on":[22,175],"Behavior":[23],"Cloning":[24],"(BC),":[25],"a":[26,54,97,108,115,181],"paradigm":[27],"that":[28,159],"suffers":[29],"from":[30],"compounding":[31],"errors":[32],"and":[33,94,126,144],"distributional":[34,129],"shift.":[35],"Consequently,":[36],"efficacy":[38],"these":[40,50],"practical":[43,182],"industrial":[44,190],"deployments":[45],"remains":[46],"limited.":[47],"To":[48],"address":[49,127],"challenges,":[51],"we":[52,113,136],"introduce":[53],"novel,":[55],"plug-and-play":[56],"fine-tuning":[57,79],"pipeline":[58],"designed":[59,121],"to":[60,74,96,122,140],"facilitate":[61],"robust":[63],"deployment":[64],"Vision-Language-Action":[66],"(VLA)":[67],"real-world":[70,153,165],"environments.":[71],"In":[72],"contrast":[73],"contemporary":[75],"reinforcement":[76],"(RL)":[78],"strategies,":[80],"which":[81,112],"are":[82],"often":[83],"constrained":[84],"by":[85],"specific":[86],"model":[87],"architectures,":[88],"our":[89],"proposed":[90],"framework":[91],"is":[92,120],"model-agnostic":[93],"adaptable":[95],"diverse":[98],"range":[99],"VLA":[101],"models.":[102],"We":[103,148],"conceptualize":[104],"VLA-generated":[105],"actions":[106,125],"as":[107],"unified":[109],"interface,":[110],"upon":[111],"train":[114],"residual":[116],"policy.":[117],"This":[118],"policy":[119],"rectify":[123],"suboptimal":[124],"shift":[130],"inherent":[131],"learning.":[134],"Additionally,":[135],"incorporate":[137],"human-in-the-loop":[138],"guidance":[139],"ensure":[141],"safe":[142],"exploration":[143],"maximize":[145],"training":[146],"efficiency.":[147],"conduct":[149],"experiments":[150],"directly":[151],"settings.":[155],"The":[156],"results":[157],"demonstrate":[158],"within":[160],"only":[161],"1.5":[162],"hour":[163],"online":[166],"RL":[167],"training,":[168],"average":[170],"success":[171],"rate":[172],"exceeds":[173],"95%":[174],"real":[176],"robots.":[177],"Our":[178],"work":[179],"presents":[180],"solution":[183],"for":[184],"deploying":[185],"behavior":[186],"cloning":[187],"scenarios.":[191]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}