{"id":"https://openalex.org/W7162688985","doi":"https://doi.org/10.48550/arxiv.2605.27724","title":"HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning","display_name":"HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162688985","doi":"https://doi.org/10.48550/arxiv.2605.27724"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27724","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.2605.27724","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074764224","display_name":"Kevin Lin","orcid":"https://orcid.org/0000-0002-1236-9847"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Kevin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026180478","display_name":"Ajay Mandlekar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mandlekar, Ajay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015917510","display_name":"Caelan Reed Garrett","orcid":"https://orcid.org/0000-0002-6474-1276"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garrett, Caelan Reed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026226476","display_name":"Nikita Chernyadev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chernyadev, Nikita","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137280429","display_name":"Yu Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137293874","display_name":"Runyu Ding","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Runyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137276699","display_name":"Yuqi Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Yuqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137301034","display_name":"Justin Tran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tran, Justin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137297542","display_name":"Linxi Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Linxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137231023","display_name":"Yuke Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yuke","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":0.4018000066280365,"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":0.4018000066280365,"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/T10879","display_name":"Robotic Locomotion and Control","score":0.32330000400543213,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T12290","display_name":"Human Motion and Animation","score":0.07729999721050262,"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/humanoid-robot","display_name":"Humanoid robot","score":0.7444000244140625},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7085999846458435},{"id":"https://openalex.org/keywords/interleaving","display_name":"Interleaving","score":0.6830999851226807},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.5569000244140625},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4763000011444092},{"id":"https://openalex.org/keywords/data-driven","display_name":"Data-driven","score":0.4677000045776367},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.45080000162124634},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4462999999523163}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7746000289916992},{"id":"https://openalex.org/C60692881","wikidata":"https://www.wikidata.org/wiki/Q584529","display_name":"Humanoid robot","level":3,"score":0.7444000244140625},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7085999846458435},{"id":"https://openalex.org/C28034677","wikidata":"https://www.wikidata.org/wiki/Q17092530","display_name":"Interleaving","level":2,"score":0.6830999851226807},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6071000099182129},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.57669997215271},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.5569000244140625},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4763000011444092},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.45080000162124634},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4462999999523163},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.35589998960494995},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32030001282691956},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3154999911785126},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C2779038628","wikidata":"https://www.wikidata.org/wiki/Q7248497","display_name":"Programming by demonstration","level":3,"score":0.3052999973297119},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27724","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.2605.27724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27724","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Imitation":[0],"learning":[1,145,157],"is":[2],"a":[3,18,61,76,120,148],"promising":[4],"approach":[5],"for":[6,39,63,143],"training":[7],"humanoid":[8,65,131],"robots":[9],"to":[10,28,81],"both":[11],"walk":[12],"and":[13,26,56,94,100,112,146,155],"manipulate,":[14],"but":[15,41],"it":[16],"requires":[17],"large":[19,141],"number":[20],"of":[21,78,151],"demonstrations,":[22],"which":[23],"are":[24,43],"time-intensive":[25],"difficult":[27],"collect":[29],"via":[30],"teleoperation.":[31],"Existing":[32],"data-generation":[33],"algorithms":[34],"can":[35],"automatically":[36,139],"synthesize":[37],"demonstrations":[38,80],"manipulators,":[40],"they":[42],"ineffective":[44],"on":[45,178],"humanoids":[46],"because":[47],"their":[48],"high-dimensional":[49],"composite":[50],"action":[51],"spaces":[52],"involve":[53],"arms,":[54],"legs,":[55],"torsos.":[57],"We":[58,162],"present":[59],"HumanoidMimicGen,":[60],"method":[62,70,104],"generating":[64],"legged":[66],"loco-manipulation":[67,123,132],"data.":[68],"Our":[69],"adapts":[71],"contact-rich":[72],"whole-body":[73,98,165],"skills":[74,96],"from":[75],"handful":[77],"source":[79],"new":[82,121],"states,":[83],"generalizing":[84],"across":[85,109],"changes":[86],"in":[87],"object":[88],"pose.":[89],"By":[90],"interleaving":[91],"these":[92],"single-":[93],"dual-arm":[95],"with":[97,169],"locomotion":[99],"manipulation":[101],"planning,":[102],"the":[103],"generates":[105,140],"stable,":[106],"collision-free":[107],"data":[108,153,170,180],"diverse":[110,127],"scenes":[111],"layouts.":[113],"To":[114],"evaluate":[115],"our":[116],"approach,":[117],"we":[118,135],"introduce":[119],"simulated":[122],"benchmark":[124],"containing":[125],"nine":[126],"tasks":[128],"that":[129,137,164],"test":[130],"capabilities.":[133],"There,":[134],"demonstrate":[136],"HumanoidMimicGen":[138,173],"datasets":[142],"imitation":[144],"enables":[147],"systematic":[149],"study":[150],"how":[152],"generation":[154],"policy":[156],"decisions":[158],"impact":[159],"model":[160],"performance.":[161],"show":[163],"visuomotor":[166],"policies":[167],"co-trained":[168],"generated":[171],"by":[172,181],"outperform":[174],"those":[175],"trained":[176],"only":[177],"real-world":[179],"20%.":[182]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-29T00:00:00"}
