{"id":"https://openalex.org/W7166075846","doi":"https://doi.org/10.48550/arxiv.2606.26839","title":"Ordinal Neural Collapse as a Representation Prior for Visual Navigation","display_name":"Ordinal Neural Collapse as a Representation Prior for Visual Navigation","publication_year":2026,"publication_date":"2026-06-25","ids":{"openalex":"https://openalex.org/W7166075846","doi":"https://doi.org/10.48550/arxiv.2606.26839"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.26839","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26839","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.26839","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112609992","display_name":"E J Son","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Son, E-In","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139184329","display_name":"Jung-Taak Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Jung-Taak","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139447870","display_name":"Seung-Woo Seo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seo, Seung-Woo","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.36329999566078186,"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":0.36329999566078186,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.2045000046491623,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.07440000027418137,"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/discriminative-model","display_name":"Discriminative model","score":0.6014999747276306},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5432000160217285},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5356000065803528},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5159000158309937},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.44909998774528503},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.4050000011920929},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.40450000762939453},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.39399999380111694},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.36809998750686646}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7311999797821045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7146000266075134},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6014999747276306},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5432000160217285},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5356000065803528},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5159000158309937},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.44909998774528503},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.39399999380111694},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3637999892234802},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.32910001277923584},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30169999599456787},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.2791000008583069},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C2779321571","wikidata":"https://www.wikidata.org/wiki/Q7936605","display_name":"Visual learning","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C111370547","wikidata":"https://www.wikidata.org/wiki/Q7451120","display_name":"Sensory cue","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.26839","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26839","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.26839","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26839","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":0.6232689619064331,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"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],"robust":[1],"navigation":[2,84,91,103,133,200,228],"policies":[3],"directly":[4],"from":[5,144],"visual":[6,22,79,161],"observations":[7],"remains":[8],"a":[9,31,118,151,180,198],"fundamental":[10],"challenge":[11],"in":[12,50,65,155,169,211,227,237],"vision-based":[13],"robotic":[14],"navigation.":[15],"In":[16,135],"end-to-end":[17,222],"imitation":[18],"learning":[19,53],"approaches,":[20],"the":[21,43,51,76,90,123,129,136,203],"encoder":[23,52,193],"and":[24,68,202,214,223,231],"action":[25,33],"decoder":[26],"are":[27],"jointly":[28],"optimized":[29],"using":[30],"single":[32,181],"loss,":[34],"which":[35,156],"provides":[36],"only":[37],"an":[38],"indirect":[39,46],"supervisory":[40],"signal":[41],"to":[42,82,93,102,128,147,175],"encoder.":[44],"This":[45],"supervision":[47],"frequently":[48],"results":[49],"ambiguous,":[54],"action-agnostic":[55,87],"representations.":[56],"The":[57,191],"problem":[58],"is":[59,194,206],"further":[60],"complicated":[61],"by":[62],"substantial":[63],"variations":[64],"scene":[66],"structure":[67,131],"appearance":[69],"across":[70],"diverse":[71],"environments,":[72],"as":[73,75,242],"well":[74],"prevalence":[77],"of":[78,132,138],"distractors":[80],"inherent":[81],"real-world":[83,215],"settings.":[85],"Such":[86],"features":[88],"cause":[89],"policy":[92],"produce":[94],"inconsistent":[95],"actions":[96],"at":[97],"ambiguous":[98],"decision":[99],"points,":[100],"leading":[101],"failure.":[104],"To":[105],"overcome":[106],"these":[107],"limitations,":[108],"we":[109],"propose":[110],"ORION":[111,219],"(Ordinal":[112],"Neural":[113],"Collapse":[114],"for":[115],"Visual":[116],"Navigation),":[117],"method":[119],"that":[120,218],"explicitly":[121],"organizes":[122],"encoder's":[124],"representation":[125],"space":[126],"according":[127],"ordinal":[130,153],"actions.":[134],"context":[137],"goal-directed":[139],"navigation,":[140],"ego-centric":[141],"control":[142],"categories":[143],"Far":[145,148],"Left":[146],"Right":[149],"exhibit":[150],"natural":[152],"relationship":[154],"neighboring":[157],"classes":[158,166],"share":[159],"similar":[160],"contexts,":[162],"while":[163,184],"semantically":[164],"opposing":[165],"differ":[167],"substantially":[168],"appearance.":[170],"We":[171],"encourage":[172],"class":[173],"representations":[174],"be":[176],"arranged":[177],"sequentially":[178],"along":[179],"discriminative":[182],"axis,":[183],"suppressing":[185],"off-axis":[186],"variance":[187],"within":[188],"each":[189],"class.":[190],"pretrained":[192],"then":[195],"integrated":[196],"into":[197],"diffusion-based":[199],"framework,":[201],"full":[204],"pipeline":[205],"fine-tuned":[207],"end-to-end.":[208],"Extensive":[209],"experiments":[210],"both":[212],"simulation":[213],"settings":[216],"show":[217],"consistently":[220],"outperforms":[221],"neural":[224],"collapse":[225],"baselines":[226],"success":[229],"rate":[230],"goal":[232],"progress,":[233],"with":[234],"notable":[235],"gains":[236],"visually":[238],"challenging":[239],"scenarios":[240],"such":[241],"complex":[243],"multi-way":[244],"intersections.":[245]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-27T00:00:00"}
