{"id":"https://openalex.org/W4310286397","doi":"https://doi.org/10.48550/arxiv.2211.14296","title":"A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation","display_name":"A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation","publication_year":2022,"publication_date":"2022-11-25","ids":{"openalex":"https://openalex.org/W4310286397","doi":"https://doi.org/10.48550/arxiv.2211.14296"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2211.14296","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.14296","pdf_url":"https://arxiv.org/pdf/2211.14296","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2211.14296","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104017904","display_name":"Hiroki Furuta","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Furuta, Hiroki","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063925941","display_name":"Yusuke Iwasawa","orcid":"https://orcid.org/0000-0002-1321-2622"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Iwasawa, Yusuke","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074059447","display_name":"Yutaka Matsuo","orcid":"https://orcid.org/0000-0002-2070-4393"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matsuo, Yutaka","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5061613634","display_name":"Shixiang Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Shixiang Shane","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5104017904"],"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":0.9897000193595886,"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":0.9897000193595886,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9887999892234802,"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/T10028","display_name":"Topic Modeling","score":0.982699990272522,"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/computer-science","display_name":"Computer science","score":0.7720731496810913},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.506234347820282},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5021142959594727},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.48366865515708923},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4664211869239807},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.44703027606010437},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44299226999282837},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.42590904235839844}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7720731496810913},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.506234347820282},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5021142959594727},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.48366865515708923},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4664211869239807},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.44703027606010437},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44299226999282837},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.42590904235839844},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2211.14296","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.14296","pdf_url":"https://arxiv.org/pdf/2211.14296","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2211.14296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2211.14296","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2211.14296","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.14296","pdf_url":"https://arxiv.org/pdf/2211.14296","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3162204513","https://openalex.org/W2371138613","https://openalex.org/W2048963458","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W2239445980","https://openalex.org/W2080152487","https://openalex.org/W3083152911","https://openalex.org/W3022347918","https://openalex.org/W4200527723"],"abstract_inverted_index":{"The":[0],"rise":[1],"of":[2,47,58,110],"generalist":[3],"large-scale":[4,103],"models":[5],"in":[6,24,92,164],"natural":[7],"language":[8],"and":[9,72,90,118,124,153,179,182,194],"vision":[10],"has":[11],"made":[12],"us":[13],"expect":[14],"that":[15,43,132],"a":[16,36,40,55,93,115,133,189],"massive":[17],"data-driven":[18],"approach":[19,191],"could":[20],"achieve":[21],"broader":[22],"generalization":[23],"other":[25,147],"domains":[26],"such":[27],"as":[28],"continuous":[29],"control.":[30],"In":[31,62],"this":[32],"work,":[33],"we":[34,82,129],"explore":[35],"method":[37],"for":[38,101,158,192],"learning":[39,187],"single":[41],"policy":[42,180],"manipulates":[44],"various":[45,51],"forms":[46],"agents":[48],"to":[49,64,146],"solve":[50],"tasks":[52,71],"by":[53],"distilling":[54],"large":[56,172],"amount":[57],"proficient":[59],"behavioral":[60],"data.":[61],"order":[63],"align":[65],"input-output":[66],"(IO)":[67],"interface":[68],"among":[69],"multiple":[70],"diverse":[73,111,173],"agent":[74],"morphologies":[75],"while":[76],"preserving":[77],"essential":[78],"3D":[79],"geometric":[80],"relations,":[81],"introduce":[83],"morphology-task":[84,112,134,196],"graph,":[85],"which":[86,106],"treats":[87],"observations,":[88],"actions":[89],"goals/task":[91],"unified":[94,176],"graph":[95,135],"representation.":[96],"We":[97],"also":[98],"develop":[99],"MxT-Bench":[100],"fast":[102],"behavior":[104],"generation,":[105],"supports":[107],"procedural":[108],"generation":[109],"combinations":[113],"with":[114,138],"minimal":[116],"blueprint":[117],"hardware-accelerated":[119],"simulator.":[120],"Through":[121],"efficient":[122],"representation":[123,136,181],"architecture":[125,140,183],"selection":[126,184],"on":[127],"MxT-Bench,":[128],"find":[130],"out":[131],"coupled":[137],"Transformer":[139],"improves":[141],"the":[142],"multi-task":[143,166],"performances":[144],"compared":[145],"baselines":[148],"including":[149],"recent":[150],"discrete":[151],"tokenization,":[152],"provides":[154],"better":[155],"prior":[156],"knowledge":[157],"zero-shot":[159],"transfer":[160],"or":[161],"sample":[162],"efficiency":[163],"downstream":[165],"imitation":[167],"learning.":[168],"Our":[169],"work":[170],"suggests":[171],"offline":[174],"datasets,":[175],"IO":[177],"representation,":[178],"through":[185],"supervised":[186],"form":[188],"promising":[190],"studying":[193],"advancing":[195],"generalization.":[197]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2022-11-30T00:00:00"}
