{"id":"https://openalex.org/W7134850453","doi":"https://doi.org/10.48550/arxiv.2603.08476","title":"LAR-MoE: Latent-Aligned Routing for Mixture of Experts in Robotic Imitation Learning","display_name":"LAR-MoE: Latent-Aligned Routing for Mixture of Experts in Robotic Imitation Learning","publication_year":2026,"publication_date":"2026-03-09","ids":{"openalex":"https://openalex.org/W7134850453","doi":"https://doi.org/10.48550/arxiv.2603.08476"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.08476","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124408644","display_name":"Ariel Rodriguez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rodriguez, Ariel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039592979","display_name":"Chenpan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chenpan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124299494","display_name":"Lorenzo Mazza","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mazza, Lorenzo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058836672","display_name":"Rayan Younis","orcid":"https://orcid.org/0000-0002-7558-8043"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Younis, Rayan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128672934","display_name":"Ortrun Hellig","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hellig, Ortrun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034437528","display_name":"Sebastian Bodenstedt","orcid":"https://orcid.org/0000-0002-2203-9729"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bodenstedt, Sebastian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128668507","display_name":"Martin Wagner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wagner, Martin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128648559","display_name":"Stefanie Speidel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Speidel, Stefanie","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26223887,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.3977999985218048,"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":0.3977999985218048,"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":0.2581999897956848,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.12809999287128448,"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/routing","display_name":"Routing (electronic design automation)","score":0.5996000170707703},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.5497000217437744},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.510699987411499},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5047000050544739},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.4921000003814697},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4318999946117401},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.41760000586509705},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.41359999775886536},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4077000021934509}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7095999717712402},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6596999764442444},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6061999797821045},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.5996000170707703},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.5497000217437744},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.510699987411499},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5047000050544739},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4921000003814697},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4318999946117401},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.41760000586509705},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4077000021934509},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.33880001306533813},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C132758656","wikidata":"https://www.wikidata.org/wiki/Q5307365","display_name":"Dreyfus model of skill acquisition","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.08476","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.08476","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08476","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":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.08476","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6903408765792847}],"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],"enables":[2],"robots":[3],"to":[4,24,96,157,172],"acquire":[5],"manipulation":[6],"skills":[7],"from":[8,68,180],"demonstrations,":[9],"yet":[10],"deploying":[11],"a":[12,60,75,88,127,136,145,169],"policy":[13,69],"across":[14],"tasks":[15],"with":[16,132],"heterogeneous":[17],"dynamics":[18],"remains":[19],"challenging,":[20],"as":[21],"models":[22],"tend":[23],"average":[25,129],"over":[26],"distinct":[27],"behavioral":[28],"modes":[29],"present":[30],"in":[31,115],"the":[32,91,98,101,121],"demonstrations.":[33,182],"Mixture-of-Experts":[34],"(MoE)":[35],"architectures":[36],"address":[37],"this":[38],"by":[39],"activating":[40],"specialized":[41],"subnetworks,":[42],"but":[43],"requires":[44],"meaningful":[45],"skill":[46,66,174],"decompositions":[47],"for":[48,55],"expert":[49,92,106,178],"routing.":[50],"We":[51,112],"introduce":[52],"Latent-Aligned":[53],"Routing":[54],"Mixture":[56],"of":[57,100],"Experts":[58],"(LAR-MoE),":[59],"two-stage":[61],"framework":[62],"that":[63,165],"decouples":[64],"unsupervised":[65],"discovery":[67],"learning.":[70],"In":[71,87],"pre-training,":[72],"we":[73],"learn":[74],"joint":[76],"latent":[77,103],"representation":[78],"between":[79],"observations":[80],"and":[81,117,140,154],"future":[82],"actions":[83],"through":[84],"student-teacher":[85],"co-training.":[86],"post-training":[89],"stage,":[90],"routing":[93,167],"is":[94],"regularized":[95],"follow":[97],"structure":[99],"learned":[102],"space,":[104],"preventing":[105],"collapse":[107],"while":[108],"maintaining":[109],"parameter":[110],"efficiency.":[111],"evaluate":[113],"LAR-MoE":[114,143],"simulation":[116],"on":[118],"hardware.":[119],"On":[120,135],"LIBERO":[122],"benchmark,":[123],"our":[124],"method":[125],"achieves":[126],"95.2%":[128],"success":[130],"rate":[131],"150M":[133],"parameters.":[134],"surgical":[137],"bowel":[138],"grasping":[139],"retraction":[141],"task,":[142],"matches":[144],"supervised":[146,173],"MoE":[147],"baseline":[148],"without":[149],"requiring":[150],"any":[151],"phase":[152],"annotations,":[153],"transfers":[155],"zero-shot":[156],"ex":[158],"vivo":[159],"porcine":[160],"tissue.":[161],"Our":[162],"findings":[163],"suggest":[164],"latent-aligned":[166],"provides":[168],"principled":[170],"alternative":[171],"decomposition,":[175],"enabling":[176],"structured":[177],"specialization":[179],"unlabeled":[181]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-03-11T00:00:00"}
