{"id":"https://openalex.org/W7162333581","doi":"https://doi.org/10.48550/arxiv.2605.22894","title":"SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-based Humanoid Control","display_name":"SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-based Humanoid Control","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162333581","doi":"https://doi.org/10.48550/arxiv.2605.22894"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.22894","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22894","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.2605.22894","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100636382","display_name":"Jingyan Zhang","orcid":"https://orcid.org/0000-0001-6979-0969"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jingyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136950025","display_name":"Han Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136962011","display_name":"Ruichi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ruichi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136986328","display_name":"Bin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018141009","display_name":"Juze Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Juze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136908310","display_name":"Xin Chen","orcid":"https://orcid.org/0000-0002-6244-7866"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136896484","display_name":"Jingya Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jingya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136941241","display_name":"Lan Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Lan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136924869","display_name":"Jingyi Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Jingyi","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/T12290","display_name":"Human Motion and Animation","score":0.28380000591278076,"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/T12290","display_name":"Human Motion and Animation","score":0.28380000591278076,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.15039999783039093,"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/T10709","display_name":"Social Robot Interaction and HRI","score":0.12710000574588776,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6044999957084656},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5992000102996826},{"id":"https://openalex.org/keywords/humanoid-robot","display_name":"Humanoid robot","score":0.5902000069618225},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4480000138282776},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.42829999327659607},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.42809998989105225},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.35749998688697815},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.34619998931884766}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8163999915122986},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6044999957084656},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5992000102996826},{"id":"https://openalex.org/C60692881","wikidata":"https://www.wikidata.org/wiki/Q584529","display_name":"Humanoid robot","level":3,"score":0.5902000069618225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4519999921321869},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4480000138282776},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.42809998989105225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39890000224113464},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.35749998688697815},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3199000060558319},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.26420000195503235}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.22894","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22894","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.2605.22894","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22894","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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.49482402205467224}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Controlling":[0],"physics-based":[1,56],"humanoids":[2],"from":[3,118],"natural-language":[4],"instructions":[5],"is":[6,63],"a":[7,20,45,50,64,102,127],"critical":[8],"step":[9],"toward":[10],"general-purpose":[11],"embodied":[12],"agents.":[13],"However,":[14],"existing":[15],"methods":[16],"remain":[17],"constrained":[18],"by":[19],"tension":[21],"between":[22,90],"semantic":[23],"expressiveness":[24],"and":[25,38,75,81,93,113,154,164,183],"physical":[26,73,162,184],"feasibility,":[27],"often":[28],"failing":[29],"to":[30],"jointly":[31],"achieve":[32],"faithful":[33],"instruction":[34,155],"following,":[35],"high-quality":[36],"motion,":[37],"stable":[39],"long-horizon":[40],"control.":[41,58],"We":[42],"propose":[43,126],"SCRIPT,":[44],"scalable":[46],"diffusion":[47],"policy":[48],"with":[49,137,176,199],"multi-stage":[51],"training":[52],"framework":[53],"for":[54,206,215],"language-driven":[55],"humanoid":[57],"The":[59],"core":[60],"of":[61],"SCRIPT":[62,171],"Joint":[65],"Action-State-Text":[66],"Diffusion":[67],"Transformer":[68],"(JAST-DiT),":[69],"which":[70,107],"represents":[71],"actions,":[72],"states,":[74],"text":[76,165,179],"as":[77],"dedicated":[78],"token":[79],"streams":[80],"couples":[82],"them":[83],"through":[84],"joint":[85],"attention,":[86],"enabling":[87],"direct":[88],"interaction":[89],"language":[91],"semantics":[92],"control":[94],"dynamics.":[95],"To":[96],"stabilize":[97],"autoregressive":[98],"control,":[99],"we":[100,125],"introduce":[101],"nonlinear":[103],"history":[104],"conditioning":[105],"mechanism,":[106],"preserves":[108],"the":[109,132,146,191],"dense":[110],"recent":[111],"context":[112],"samples":[114],"increasingly":[115],"sparse":[116],"cues":[117],"long-term":[119],"history.":[120],"Beyond":[121],"supervised":[122],"imitation":[123],"pre-training,":[124],"post-training":[128],"stage,":[129],"further":[130],"improving":[131],"performance":[133,197],"using":[134,160],"Reinforcement":[135],"Learning":[136],"Hybrid":[138],"Rewards":[139],"(RLHR).":[140],"By":[141],"injecting":[142],"learnable":[143],"noise":[144],"into":[145],"flow-sampling":[147],"process,":[148],"RLHR":[149],"effectively":[150],"improves":[151],"motion":[152,181],"quality":[153],"following":[156],"within":[157],"closed-loop":[158],"simulations":[159],"hybrid":[161],"feedback":[163],"rewards.":[166],"Quantitative":[167],"evaluations":[168],"demonstrate":[169,195],"that":[170],"outperforms":[172],"prior":[173],"state-of-the-art":[174],"methods,":[175],"gains":[177,198],"across":[178],"alignment,":[180],"quality,":[182],"realism":[185],"metrics.":[186],"Furthermore,":[187],"scaling":[188],"studies":[189],"on":[190],"1200-hour":[192],"MotionMillion":[193],"dataset":[194],"consistent":[196],"model":[200],"scaling,":[201],"highlighting":[202],"SCRIPT's":[203],"robust":[204],"scalability":[205],"large-scale":[207],"pre-training.":[208],"Our":[209],"code":[210],"will":[211],"be":[212],"publicly":[213],"available":[214],"future":[216],"research.":[217]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-26T00:00:00"}