{"id":"https://openalex.org/W7160410906","doi":"https://doi.org/10.48550/arxiv.2605.03927","title":"StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning","display_name":"StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160410906","doi":"https://doi.org/10.48550/arxiv.2605.03927"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03927","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03927","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.2605.03927","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135422734","display_name":"Xiaowen Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Xiaowen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040045142","display_name":"Matthias Kerzel","orcid":"https://orcid.org/0000-0002-1378-0435"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kerzel, Matthias","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135480797","display_name":"Mengdi Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Mengdi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135504441","display_name":"Xufeng Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Xufeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135494249","display_name":"Paul Striker","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Striker, Paul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135532638","display_name":"Stefan Wermter","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wermter, Stefan","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8849999904632568,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8849999904632568,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.019500000402331352,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.014999999664723873,"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/affordance","display_name":"Affordance","score":0.8465999960899353},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.652899980545044},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.6211000084877014},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6028000116348267},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.5339999794960022},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5051000118255615},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.4876999855041504},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4763999879360199},{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.47119998931884766}],"concepts":[{"id":"https://openalex.org/C194995250","wikidata":"https://www.wikidata.org/wiki/Q531136","display_name":"Affordance","level":2,"score":0.8465999960899353},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7556999921798706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6883999705314636},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.652899980545044},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.6211000084877014},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6028000116348267},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.5339999794960022},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5170000195503235},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5051000118255615},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4876999855041504},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4763999879360199},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.43709999322891235},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C111151474","wikidata":"https://www.wikidata.org/wiki/Q1653368","display_name":"iCub","level":4,"score":0.3325999975204468},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.33149999380111694},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.32510000467300415},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.28380000591278076},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.26499998569488525},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03927","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03927","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.2605.03927","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03927","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":"Quality Education","score":0.582905650138855,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-language":[0],"models":[1,38,189,214],"(VLMs)":[2],"have":[3],"shown":[4],"remarkable":[5],"performance":[6,181,212],"in":[7,35,46,60,230],"various":[8],"robotic":[9],"tasks,":[10],"as":[11,56,129,131],"they":[12,40],"can":[13],"perceive":[14,115],"visual":[15],"information":[16],"and":[17,49,74,116,126,163,174],"understand":[18],"natural":[19],"language":[20,37],"instructions.":[21],"However,":[22],"when":[23],"applied":[24],"to":[25,30,68,83,104,114,135,188],"robotics,":[26],"VLMs":[27,70],"remain":[28],"subject":[29],"a":[31,57,64,110,139],"fundamental":[32],"limitation":[33],"inherent":[34],"large":[36],"(LLMs):":[39],"struggle":[41],"with":[42,159,204],"numerical":[43,54],"reasoning,":[44,144],"particularly":[45],"object":[47,72,119],"detection":[48,73],"object-state":[50,75,142],"localization.":[51,76],"To":[52],"explore":[53],"reasoning":[55,229],"regression":[58],"task":[59,226],"VLMs,":[61],"we":[62,145],"propose":[63],"novel":[65,111],"training":[66,102],"strategy":[67,103],"adapt":[69],"for":[71,141,223],"This":[77],"approach":[78],"leverages":[79],"box":[80],"decoder":[81],"outputs":[82],"compute":[84],"an":[85,147,183,207],"Auxiliary":[86],"Regression":[87],"Loss":[88],"(ARL)":[89],"during":[90],"fine-tuning,":[91],"while":[92],"preserving":[93],"standard":[94],"sequence":[95],"prediction":[96],"at":[97],"inference.":[98],"We":[99],"leverage":[100],"this":[101,199],"develop":[105],"StateVLM":[106,203],"(State-aware":[107],"Vision-Language":[108],"Model),":[109],"model":[112,180,238],"designed":[113],"learn":[117],"fine-grained":[118],"representations,":[120],"including":[121],"precise":[122],"localization":[123],"of":[124,138,185,209,227,237],"objects":[125,162],"their":[127],"states,":[128],"well":[130],"graspable":[132],"regions.":[133],"Due":[134],"the":[136,194,224,235],"lack":[137],"benchmark":[140,196],"affordance":[143,228],"introduce":[146],"open-source":[148],"benchmark,":[149],"Object":[150],"State":[151],"Affordance":[152],"Reasoning":[153],"(OSAR),":[154],"which":[155],"contains":[156],"1172":[157],"scenes":[158],"7746":[160],"individual":[161],"corresponding":[164],"bounding":[165],"boxes.":[166],"Comparative":[167],"experiments":[168],"on":[169,193],"adapted":[170],"benchmarks":[171],"(RefCOCO,":[172],"RefCOCO+,":[173],"RefCOCOg)":[175],"demonstrate":[176],"that":[177,202],"ARL":[178,205,219],"improves":[179],"by":[182],"average":[184,208],"1.6%":[186],"compared":[187],"without":[190,215],"ARL.":[191,216],"Experiments":[192],"OSAR":[195],"further":[197],"support":[198],"finding,":[200],"showing":[201],"achieves":[206],"5.2%":[210],"higher":[211],"than":[213],"In":[217],"particular,":[218],"is":[220],"also":[221],"important":[222],"complex":[225],"OSAR,":[231],"where":[232],"it":[233],"enhances":[234],"consistency":[236],"outputs.":[239]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-07T00:00:00"}
