{"id":"https://openalex.org/W7155063202","doi":"https://doi.org/10.48550/arxiv.2604.17896","title":"Can Explicit Physical Feasibility Benefit VLA Learning? An Empirical Study","display_name":"Can Explicit Physical Feasibility Benefit VLA Learning? An Empirical Study","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7155063202","doi":"https://doi.org/10.48550/arxiv.2604.17896"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.17896","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17896","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":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.2604.17896","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134122518","display_name":"Yubai Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Yubai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134130174","display_name":"Chen Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5118537482","display_name":"Hashem Haghbayan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haghbayan, Hashem","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/T10653","display_name":"Robot Manipulation and Learning","score":0.5760999917984009,"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"}},"topics":[{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.5760999917984009,"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":0.2337999939918518,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.053599998354911804,"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/reliability","display_name":"Reliability (semiconductor)","score":0.6236000061035156},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.5979999899864197},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.5924999713897705},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5857999920845032},{"id":"https://openalex.org/keywords/kinematics","display_name":"Kinematics","score":0.5386000275611877},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.44119998812675476},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.43849998712539673},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4357999861240387}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.671999990940094},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6236000061035156},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.5979999899864197},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.5924999713897705},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5857999920845032},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.5386000275611877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4903999865055084},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.44119998812675476},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4357999861240387},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3885999917984009},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3847000002861023},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.34880000352859497},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C161759796","wikidata":"https://www.wikidata.org/wiki/Q3982902","display_name":"Teleoperation","level":3,"score":0.30790001153945923},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.27799999713897705},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2639999985694885}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.17896","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17896","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":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.2604.17896","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17896","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-Language-Action":[0],"(VLA)":[1],"models":[2],"map":[3],"multimodal":[4],"inputs":[5],"directly":[6],"to":[7],"robot":[8],"actions":[9],"and":[10,85,128],"are":[11],"typically":[12],"trained":[13],"through":[14],"large-scale":[15],"imitation":[16],"learning.":[17],"While":[18],"this":[19,61,99],"paradigm":[20],"has":[21],"shown":[22],"strong":[23],"performance,":[24,131],"prevailing":[25],"VLA":[26,76,95,119,152,161],"training":[27,90,120],"procedures":[28],"do":[29],"not":[30],"explicitly":[31],"supervise":[32],"hard":[33],"physical":[34,112,126],"constraints":[35],"such":[36],"as":[37,106],"obstacle":[38],"avoidance":[39],"or":[40],"kinematic":[41],"feasibility.":[42,113],"As":[43],"a":[44,80,93,107],"result,":[45],"the":[46,89,138],"geometric":[47],"structure":[48],"underlying":[49],"physically":[50],"feasible":[51],"behavior":[52],"must":[53],"be":[54],"inferred":[55],"only":[56],"implicitly":[57],"from":[58],"demonstrations.":[59],"In":[60],"paper,":[62],"we":[63,102],"study":[64],"whether":[65],"introducing":[66],"explicit":[67,145],"feasibility":[68,83,122,146],"supervision":[69,123],"can":[70,148],"provide":[71],"effective":[72],"structured":[73],"guidance":[74],"for":[75,157],"policies.":[77,162],"We":[78],"formulate":[79],"simple":[81],"geometry-grounded":[82],"objective":[84],"integrate":[86],"it":[87],"into":[88],"stage":[91],"of":[92,110],"diffusion-based":[94],"policy.":[96],"To":[97],"evaluate":[98],"idea":[100],"systematically,":[101],"use":[103],"obstacle-aware":[104],"manipulation":[105],"controlled":[108],"probe":[109],"geometry-dependent":[111],"Empirical":[114],"results":[115],"show":[116],"that":[117,144],"augmenting":[118],"with":[121],"improves":[124],"both":[125],"reliability":[127],"overall":[129],"task":[130],"while":[132],"also":[133],"enhancing":[134],"learning":[135],"efficiency":[136],"in":[137],"low-data":[139],"regime.":[140],"These":[141],"findings":[142],"indicate":[143],"signals":[147],"effectively":[149],"complement":[150],"imitation-based":[151],"learning,":[153],"highlighting":[154],"their":[155],"potential":[156],"developing":[158],"more":[159],"reliable":[160]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-22T00:00:00"}