{"id":"https://openalex.org/W7160271768","doi":"https://doi.org/10.48550/arxiv.2605.02699","title":"Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions","display_name":"Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160271768","doi":"https://doi.org/10.48550/arxiv.2605.02699"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.02699","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02699","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.2605.02699","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135347004","display_name":"Sergio Orozco","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Orozco, Sergio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135415837","display_name":"Tushar Kusnur","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kusnur, Tushar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135363382","display_name":"Brandon May","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"May, Brandon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135336435","display_name":"George Konidaris","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Konidaris, George","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123900359","display_name":"Laura Herlant","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Herlant, Laura","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":0.727400004863739,"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":0.727400004863739,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.05820000171661377,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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":0.031300000846385956,"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/object","display_name":"Object (grammar)","score":0.6363000273704529},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6312000155448914},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5871999859809875},{"id":"https://openalex.org/keywords/dynamics","display_name":"Dynamics (music)","score":0.5232999920845032},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5004000067710876},{"id":"https://openalex.org/keywords/equivariant-map","display_name":"Equivariant map","score":0.4799000024795532},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.46230000257492065},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44510000944137573}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6643000245094299},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6363000273704529},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6312000155448914},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6147000193595886},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5871999859809875},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.5232999920845032},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5004000067710876},{"id":"https://openalex.org/C171036898","wikidata":"https://www.wikidata.org/wiki/Q256355","display_name":"Equivariant map","level":2,"score":0.4799000024795532},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.46230000257492065},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44510000944137573},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43939998745918274},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.40619999170303345},{"id":"https://openalex.org/C96469262","wikidata":"https://www.wikidata.org/wiki/Q1324364","display_name":"Homogeneous space","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.3246999979019165},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3138999938964844},{"id":"https://openalex.org/C28461519","wikidata":"https://www.wikidata.org/wiki/Q2037529","display_name":"Rigid body dynamics","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C150415221","wikidata":"https://www.wikidata.org/wiki/Q40687","display_name":"Robotic arm","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.27810001373291016},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.02699","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02699","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.2605.02699","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02699","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":[{"score":0.457253098487854,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Learning":[0],"data-efficient":[1],"object":[2,72],"dynamics":[3,73,159],"models":[4,69],"for":[5,11,74,139],"robotic":[6,164],"manipulation":[7,165],"remains":[8],"challenging,":[9],"especially":[10],"deformable":[12,78],"objects.":[13,151],"A":[14],"popular":[15],"approach":[16,62],"is":[17,37],"to":[18,40,55,63,70,102,125],"model":[19,96],"objects":[20],"as":[21,98],"sets":[22],"of":[23,52,87,170],"3D":[24],"particles":[25],"and":[26,47,67,77,107,135,141,149,161],"learn":[27],"their":[28],"motion":[29],"using":[30,80],"graph":[31],"neural":[32],"networks.":[33],"In":[34],"practice,":[35],"this":[36],"not":[38],"enough":[39],"maintain":[41],"physical":[42],"feasibility":[43],"over":[44],"long":[45],"horizons":[46],"may":[48],"require":[49],"large":[50],"amounts":[51],"interaction":[53,83],"data":[54],"learn.":[56],"We":[57,130,152],"introduce":[58],"PIEGraph,":[59],"a":[60,91,99,115],"novel":[61,116],"combining":[64],"analytical":[65,95,128],"physics":[66],"data-driven":[68],"capture":[71],"both":[75],"rigid":[76,150],"bodies":[79],"limited":[81],"real-world":[82],"data.":[84],"PIEGraph":[85,132],"consists":[86],"two":[88],"components:":[89],"(1)":[90],"\\textbf{P}hysically":[92],"\\textbf{I}nformed":[93],"particle-based":[94],"(implemented":[97],"spring--mass":[100],"system)":[101],"enforce":[103],"physically":[104],"feasible":[105],"motion,":[106],"(2)":[108],"an":[109],"\\textbf{E}quivariant":[110],"\\textbf{Graph}":[111],"Neural":[112],"Network":[113],"with":[114,144],"action":[117],"representation":[118],"that":[119,154],"exploits":[120],"symmetries":[121],"in":[122,133],"particle":[123],"interactions":[124],"guide":[126],"the":[127,171],"model.":[129],"evaluate":[131],"simulation":[134],"on":[136],"robot":[137],"hardware":[138],"reorientation":[140],"repositioning":[142],"tasks":[143],"ropes,":[145],"cloth,":[146],"stuffed":[147],"animals":[148],"show":[153],"our":[155],"method":[156],"enables":[157],"accurate":[158],"prediction":[160],"reliable":[162],"downstream":[163],"planning,":[166],"which":[167],"outperforms":[168],"state":[169],"art":[172],"baselines.":[173]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}