{"id":"https://openalex.org/W7164810171","doi":"https://doi.org/10.48550/arxiv.2606.13769","title":"$\u03bc_0$: A Scalable 3D Interaction-Trace World Model","display_name":"$\u03bc_0$: A Scalable 3D Interaction-Trace World Model","publication_year":2026,"publication_date":"2026-06-11","ids":{"openalex":"https://openalex.org/W7164810171","doi":"https://doi.org/10.48550/arxiv.2606.13769"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.13769","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13769","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.13769","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138671858","display_name":"Seungjae Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Seungjae","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138641312","display_name":"Yoonkyo Jung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jung, Yoonkyo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014563296","display_name":"Jusuk Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Jusuk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137346529","display_name":"Jonghun Shin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shin, Jonghun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004075943","display_name":"Amir Hossein Shahidzadeh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shahidzadeh, Amir Hossein","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138660678","display_name":"Yao-Chih Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Yao-Chih","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138629963","display_name":"H. Jin Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, H. Jin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006022044","display_name":"J W Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Jia-Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082387577","display_name":"F T Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Furong","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":0.32100000977516174,"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":0.32100000977516174,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.31769999861717224,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.10819999873638153,"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/trace","display_name":"TRACE (psycholinguistics)","score":0.7809000015258789},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7355999946594238},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.6316999793052673},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5333999991416931},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5270000100135803},{"id":"https://openalex.org/keywords/hierarchy","display_name":"Hierarchy","score":0.48240000009536743},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.45680001378059387},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4546999931335449}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7979000210762024},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.7809000015258789},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7355999946594238},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.6316999793052673},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5615000128746033},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5333999991416931},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5270000100135803},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.48240000009536743},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.45680001378059387},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4546999931335449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41679999232292175},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.4058000147342682},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.4056999981403351},{"id":"https://openalex.org/C2779038628","wikidata":"https://www.wikidata.org/wiki/Q7248497","display_name":"Programming by demonstration","level":3,"score":0.3986999988555908},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3935000002384186},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.38589999079704285},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3197999894618988},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2851000130176544},{"id":"https://openalex.org/C2777897806","wikidata":"https://www.wikidata.org/wiki/Q568742","display_name":"3D modeling","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.28220000863075256},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.13769","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13769","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.13769","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13769","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":{"World":[0],"models":[1,21,37,160,194],"that":[2,41,146],"capture":[3],"how":[4],"actions":[5],"induce":[6],"physical":[7],"change":[8],"enable":[9,88],"scalable":[10,48,209],"robot":[11,180],"learning":[12],"without":[13],"reliance":[14],"on":[15,30,52],"embodiment-specific":[16,39],"action":[17,36,176,197],"labels.":[18],"Pixel-space":[19],"video":[20,92],"provide":[22],"broad":[23],"visual":[24],"priors":[25],"but":[26],"expend":[27],"model":[28,50],"capacity":[29],"dense":[31,58],"appearance":[32],"reconstruction,":[33],"while":[34],"direct":[35],"require":[38],"labels":[40],"hinder":[42],"scalability.":[43],"We":[44],"present":[45],"$\u03bc_0$,":[46],"a":[47,82,123,128,208],"world":[49],"based":[51],"3D":[53,67,99,154,205],"traces.":[54,143],"Rather":[55],"than":[56],"predicting":[57],"pixels":[59],"or":[60],"directly":[61],"modeling":[62],"actions,":[63],"$\u03bc_0$":[64,120,147,166],"forecasts":[65],"smooth":[66],"trajectories":[68],"for":[69,178,213],"salient":[70],"interaction":[71],"points":[72,139],"such":[73,199],"as":[74,200,207],"objects,":[75],"tools,":[76],"hands,":[77],"and":[78,108,140,153,161,169,210],"contact":[79],"regions,":[80],"yielding":[81],"compact,":[83],"embodiment-agnostic":[84],"motion":[85,110],"interface.":[86],"To":[87],"training":[89],"from":[90],"diverse":[91],"sources,":[93],"our":[94],"TraceExtract":[95,117],"system":[96],"automatically":[97],"extracts":[98],"supervision":[100,118],"by":[101,121],"selecting":[102],"keypoints,":[103],"constructing":[104],"globally":[105],"aligned":[106],"traces,":[107],"associating":[109],"segments":[111],"with":[112,127,175,192,196],"hierarchical":[113],"language":[114],"captions.":[115],"This":[116],"pretrains":[119],"combining":[122],"pretrained":[124,195],"vision-language":[125],"backbone":[126],"modular":[129],"trace":[130,155,158],"expert,":[131],"which":[132],"represents":[133],"each":[134],"query":[135],"via":[136],"B-spline":[137],"control":[138],"predicts":[141],"future":[142],"Experiments":[144],"show":[145],"outperforms":[148],"baselines":[149],"in":[150],"both":[151],"2D":[152],"prediction,":[156],"including":[157],"prediction":[159],"tokenized":[162],"VLM":[163],"methods.":[164],"Because":[165],"is":[167],"frozen":[168],"reusable,":[170],"it":[171],"can":[172],"be":[173],"paired":[174],"experts":[177],"downstream":[179],"embodiments.":[181],"Despite":[182],"action-free":[183],"pretraining,":[184],"the":[185],"resulting":[186],"trace-conditioned":[187],"policies":[188],"achieve":[189],"performance":[190],"competitive":[191],"VLA":[193],"supervision,":[198],"$\u03c0_0$.":[201],"These":[202],"results":[203],"establish":[204],"traces":[206],"transferable":[211],"representation":[212],"cross-embodiment":[214],"manipulation.":[215]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-16T00:00:00"}
