{"id":"https://openalex.org/W7153223647","doi":"https://doi.org/10.48550/arxiv.2604.07945","title":"Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation","display_name":"Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7153223647","doi":"https://doi.org/10.48550/arxiv.2604.07945"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.07945","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07945","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.2604.07945","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133380608","display_name":"Haruto Nagahisa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nagahisa, Haruto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108312962","display_name":"Kohei MATSUMOTO","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matsumoto, Kohei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133393617","display_name":"Yuki Tomita","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tomita, Yuki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133390366","display_name":"Yuki Hyodo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hyodo, Yuki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133329346","display_name":"Ryo Kurazume","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kurazume, Ryo","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/T10709","display_name":"Social Robot Interaction and HRI","score":0.24869999289512634,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10709","display_name":"Social Robot Interaction and HRI","score":0.24869999289512634,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.2303999960422516,"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":0.12290000170469284,"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":0.7577000260353088},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.567300021648407},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5473999977111816},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.45320001244544983},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.40049999952316284},{"id":"https://openalex.org/keywords/incremental-learning","display_name":"Incremental learning","score":0.39320001006126404},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.3865000009536743},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.3610999882221222}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7577000260353088},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.717199981212616},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5902000069618225},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.567300021648407},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5473999977111816},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4708999991416931},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.45320001244544983},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.40049999952316284},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C188888258","wikidata":"https://www.wikidata.org/wiki/Q7353390","display_name":"Robot learning","level":4,"score":0.3447999954223633},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.33009999990463257},{"id":"https://openalex.org/C79416737","wikidata":"https://www.wikidata.org/wiki/Q2305519","display_name":"Social learning","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3021000027656555},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C10090317","wikidata":"https://www.wikidata.org/wiki/Q7551030","display_name":"Social cue","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.27070000767707825},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.2621000111103058},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26179999113082886}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.07945","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07945","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.2604.07945","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07945","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":[{"display_name":"Reduced inequalities","score":0.4746590256690979,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0],"the":[1,121,129,158,175],"demand":[2],"for":[3],"mobile":[4],"robots":[5,166],"continues":[6],"to":[7,62,124,144,167,170],"increase,":[8],"social":[9,31],"navigation":[10],"has":[11],"emerged":[12],"as":[13],"a":[14,59,98,104,125,137],"critical":[15],"task,":[16],"driving":[17],"active":[18],"research":[19],"into":[20],"deep":[21],"reinforcement":[22],"learning":[23,80,115,155],"(RL)":[24],"approaches.":[25,156],"However,":[26],"because":[27],"pedestrian":[28],"dynamics":[29],"and":[30,79,151],"conventions":[32],"vary":[33],"widely":[34],"across":[35],"different":[36],"regions,":[37],"simulations":[38],"cannot":[39],"easily":[40],"encompass":[41],"all":[42],"possible":[43],"real-world":[44,159,176],"scenarios.":[45],"Real-world":[46],"RL,":[47,112],"in":[48,55],"which":[49,96,113],"agents":[50],"learn":[51],"while":[52],"operating":[53],"directly":[54],"physical":[56],"environments,":[57],"presents":[58],"promising":[60],"solution":[61],"this":[63,66,83],"issue.":[64],"Nevertheless,":[65],"approach":[67],"faces":[68],"significant":[69],"challenges,":[70],"particularly":[71],"regarding":[72],"constrained":[73],"computational":[74],"resources":[75],"on":[76,120],"edge":[77],"devices":[78],"efficiency.":[81],"In":[82],"study,":[84],"we":[85,132],"propose":[86],"incremental":[87,94,154],"residual":[88,111],"RL":[89],"(IRRL).":[90],"This":[91],"method":[92],"integrates":[93],"learning,":[95],"is":[97],"lightweight":[99],"process":[100],"that":[101,162],"operates":[102],"without":[103],"replay":[105,138,148],"buffer":[106],"or":[107],"batch":[108],"updates,":[109],"with":[110],"enhances":[114],"efficiency":[116],"by":[117],"training":[118],"only":[119],"residuals":[122],"relative":[123],"base":[126],"policy.":[127],"Through":[128],"simulation":[130],"experiments,":[131],"demonstrated":[133],"that,":[134],"despite":[135],"lacking":[136],"buffer,":[139],"IRRL":[140,163],"achieved":[141],"performance":[142],"comparable":[143],"those":[145],"of":[146],"conventional":[147],"buffer-based":[149],"methods":[150],"outperformed":[152],"existing":[153],"Furthermore,":[157],"experiments":[160],"confirmed":[161],"can":[164],"enable":[165],"effectively":[168],"adapt":[169],"previously":[171],"unseen":[172],"environments":[173],"through":[174],"learning.":[177]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-11T00:00:00"}