{"id":"https://openalex.org/W4416749157","doi":"https://doi.org/10.1109/iros60139.2025.11246304","title":"Empirical Analysis of Sim-and-Real Cotraining of Diffusion Policies For Planar Pushing from Pixels","display_name":"Empirical Analysis of Sim-and-Real Cotraining of Diffusion Policies For Planar Pushing from Pixels","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416749157","doi":"https://doi.org/10.1109/iros60139.2025.11246304"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11246304","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246304","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Adam Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Adam Wei","raw_affiliation_strings":["Massachusetts Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082641160","display_name":"Abhinav Agarwal","orcid":"https://orcid.org/0000-0002-9467-8511"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhinav Agarwal","raw_affiliation_strings":["Massachusetts Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103094528","display_name":"Boyuan Chen","orcid":"https://orcid.org/0009-0003-4012-1659"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Boyuan Chen","raw_affiliation_strings":["Massachusetts Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114424970","display_name":"Rohan Bosworth","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rohan Bosworth","raw_affiliation_strings":["Massachusetts Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119843828","display_name":"Nicholas Pfaff","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nicholas Pfaff","raw_affiliation_strings":["Massachusetts Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074291890","display_name":"Russ Tedrake","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Russ Tedrake","raw_affiliation_strings":["Massachusetts Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":2.2326,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.90036695,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5625","last_page":"5632"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.5631999969482422,"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":0.5631999969482422,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.13830000162124634,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.051600001752376556,"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/pixel","display_name":"Pixel","score":0.5823000073432922},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5552999973297119},{"id":"https://openalex.org/keywords/planar","display_name":"Planar","score":0.5169000029563904},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4970000088214874},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.49559998512268066},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4708000123500824},{"id":"https://openalex.org/keywords/high-fidelity","display_name":"High fidelity","score":0.46869999170303345},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.45249998569488525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.722100019454956},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5823000073432922},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5552999973297119},{"id":"https://openalex.org/C134786449","wikidata":"https://www.wikidata.org/wiki/Q3391255","display_name":"Planar","level":2,"score":0.5169000029563904},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4970000088214874},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.49559998512268066},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4708000123500824},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.45249998569488525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44999998807907104},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4388999938964844},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3970000147819519},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3447999954223633},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31459999084472656},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.3068999946117401},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28940001130104065},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.25850000977516174}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11246304","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246304","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2104094955","https://openalex.org/W2194775991","https://openalex.org/W2605102758","https://openalex.org/W2962843773","https://openalex.org/W3136670918","https://openalex.org/W4385430674","https://openalex.org/W4388692454","https://openalex.org/W4402354126","https://openalex.org/W4402354156","https://openalex.org/W4402890475","https://openalex.org/W4403337227","https://openalex.org/W4413917236","https://openalex.org/W4413918134","https://openalex.org/W4414050412","https://openalex.org/W4414050931","https://openalex.org/W4416749000"],"related_works":[],"abstract_inverted_index":{"Cotraining":[0],"with":[1,50,70],"demonstration":[2],"data":[3,52,60,73,81],"generated":[4],"both":[5],"in":[6,22,163],"simulation":[7,37],"and":[8,42,139,178,187],"on":[9,149,175,183],"real":[10,59],"hardware":[11],"has":[12],"emerged":[13],"as":[14],"a":[15,76],"promising":[16],"recipe":[17],"for":[18,102],"scaling":[19],"imitation":[20],"learning":[21],"robotics.":[23],"This":[24],"work":[25],"seeks":[26],"to":[27,35,75,127,160],"elucidate":[28],"basic":[29],"principles":[30],"of":[31,153],"this":[32,83,137],"simand-real":[33],"cotraining":[34,49,117],"inform":[36],"design,":[38],"sim-and-real":[39],"dataset":[40],"creation,":[41],"policy":[43],"training.":[44],"Our":[45],"experiments":[46,169],"confirm":[47],"that":[48,65,90,111,122,141],"simulated":[51,72,129,180],"can":[53,115,189],"dramatically":[54],"improve":[55],"performance,":[56],"especially":[57],"when":[58],"is":[61],"limited.":[62],"We":[63,133],"show":[64],"these":[66],"performance":[67,84],"gains":[68],"scale":[69],"additional":[71],"up":[74],"plateau;":[77],"adding":[78],"more":[79,97],"real-world":[80,172],"increases":[82],"ceiling.":[85],"The":[86],"results":[87],"also":[88],"suggest":[89],"reducing":[91],"physical":[92],"domain":[93],"gaps":[94],"may":[95],"be":[96,161,190],"impactful":[98],"than":[99],"visual":[100,113],"fidelity":[101],"non-prehensile":[103],"or":[104],"contact-rich":[105],"tasks.":[106],"Perhaps":[107],"surprisingly,":[108],"we":[109],"find":[110],"some":[112],"gap":[114],"help":[116],"\u2013":[118],"binary":[119],"probes":[120],"reveal":[121],"high-performing":[123],"policies":[124,173,181],"must":[125],"learn":[126],"distinguish":[128],"domains":[130],"from":[131,156],"real.":[132],"conclude":[134],"by":[135],"investigating":[136],"nuance":[138],"mechanisms":[140],"facilitate":[142],"positive":[143],"transfer":[144],"between":[145],"sim-and-real.":[146],"Focusing":[147],"narrowly":[148],"the":[150],"canonical":[151],"task":[152],"planar":[154],"pushing":[155],"pixels":[157],"allows":[158],"us":[159],"thorough":[162],"our":[164,168],"study.":[165],"In":[166],"total,":[167],"span":[170],"50+":[171],"(evaluated":[174,182],"1000+":[176],"trials)":[177],"250":[179],"50,000+":[184],"trials).":[185],"Videos":[186],"code":[188],"found":[191],"at":[192],"https://sim-and-real-cotraining.github.io/.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-11-28T00:00:00"}
