{"id":"https://openalex.org/W7164203791","doi":"https://doi.org/10.48550/arxiv.2606.10366","title":"A Practical Recipe Towards Improving Sim-and-Real Correlation for VLA Evaluation","display_name":"A Practical Recipe Towards Improving Sim-and-Real Correlation for VLA Evaluation","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164203791","doi":"https://doi.org/10.48550/arxiv.2606.10366"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.10366","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10366","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.2606.10366","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138298837","display_name":"Shuo Wang (143908)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138348278","display_name":"Hanyuan Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Hanyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138390513","display_name":"Yingdong Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yingdong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138340647","display_name":"Fanqi Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Fanqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138303213","display_name":"Yang Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Yang","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":0.2971000075340271,"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":0.2971000075340271,"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":0.12460000067949295,"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":0.12330000102519989,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7652999758720398},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4050999879837036},{"id":"https://openalex.org/keywords/simulation-modeling","display_name":"Simulation modeling","score":0.40209999680519104},{"id":"https://openalex.org/keywords/policy-learning","display_name":"Policy learning","score":0.3246000111103058},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.31610000133514404},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.2865000069141388}],"concepts":[{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7652999758720398},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7214999794960022},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C108215451","wikidata":"https://www.wikidata.org/wiki/Q7263963","display_name":"Simulation modeling","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.35120001435279846},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.311599999666214},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.27480000257492065},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2718000113964081},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.26759999990463257},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.26510000228881836},{"id":"https://openalex.org/C2778671685","wikidata":"https://www.wikidata.org/wiki/Q219239","display_name":"Recipe","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C25641967","wikidata":"https://www.wikidata.org/wiki/Q7663879","display_name":"Systems simulation","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.10366","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10366","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.2606.10366","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10366","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":[{"score":0.45470595359802246,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Simulation":[0],"has":[1],"become":[2],"an":[3],"essential":[4],"tool":[5],"for":[6,46,129,155,164,170,174],"evaluating":[7],"and":[8,16,33,75,93,108,138,158,173],"improving":[9,159],"vision-language-action":[10],"(VLA)":[11],"policies,":[12,73,166],"offering":[13,167],"scalable,":[14],"reproducible,":[15],"controllable":[17],"alternatives":[18],"to":[19,101],"costly":[20],"real-world":[21,47,83,119],"robot":[22],"evaluation.":[23,49],"Recent":[24],"simulation":[25,70,113,128,163,178],"benchmarks":[26],"have":[27,38],"made":[28],"substantial":[29],"progress":[30],"on":[31],"realism":[32],"diversity,":[34],"yet":[35],"these":[36],"platforms":[37],"not":[39],"been":[40],"widely":[41],"adopted":[42],"as":[43,179],"reliable":[44],"proxies":[45],"policy":[48,88,130,183],"In":[50],"this":[51,55],"work,":[52],"we":[53],"investigate":[54],"issue":[56],"through":[57],"the":[58,103,140,160,182],"lens":[59],"of":[60,87,105,112,142,162,181],"sim-and-real":[61,146],"correlation.":[62],"We":[63,121],"conduct":[64],"a":[65,152],"systematic":[66],"study":[67],"across":[68],"multiple":[69],"platforms,":[71],"VLA":[72,165],"tasks,":[74],"perturbation":[76],"factors,":[77],"measuring":[78],"whether":[79],"simulated":[80],"evaluation":[81],"preserves":[82],"conclusions":[84],"in":[85],"terms":[86],"ranking":[89],"consistency,":[90],"performance":[91],"correlation,":[92],"perturbation-wise":[94],"failure":[95],"patterns.":[96],"This":[97],"analysis":[98],"allows":[99],"us":[100],"characterize":[102],"limitations":[104],"existing":[106],"simulators":[107],"identify":[109],"what":[110],"kinds":[111],"signals":[114],"are":[115],"more":[116],"aligned":[117],"with":[118],"deployment.":[120],"further":[122],"examine":[123],"how":[124,139],"users":[125],"should":[126],"exploit":[127],"improvement,":[131],"including":[132],"when":[133],"simulator-based":[134],"finetuning":[135],"is":[136],"beneficial":[137],"amount":[141],"post-training":[143],"data":[144],"affects":[145],"alignment.":[147],"Overall,":[148],"our":[149],"work":[150],"provides":[151],"unified":[153],"framework":[154],"measuring,":[156],"interpreting,":[157],"usefulness":[161],"guidance":[168],"both":[169],"simulator":[171],"designers":[172],"practitioners":[175],"who":[176],"use":[177],"part":[180],"development":[184],"pipeline.":[185]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-11T00:00:00"}