{"id":"https://openalex.org/W7128406468","doi":"https://doi.org/10.48550/arxiv.2602.06698","title":"Crowd-FM: Learned Optimal Selection of Conditional Flow Matching-generated Trajectories for Crowd Navigation","display_name":"Crowd-FM: Learned Optimal Selection of Conditional Flow Matching-generated Trajectories for Crowd Navigation","publication_year":2026,"publication_date":"2026-02-06","ids":{"openalex":"https://openalex.org/W7128406468","doi":"https://doi.org/10.48550/arxiv.2602.06698"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.06698","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","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/A5057814265","display_name":"A.K. Singha","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Singha, Antareep","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092029436","display_name":"Laksh Nanwani","orcid":"https://orcid.org/0009-0008-6422-1749"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nanwani, Laksh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125398008","display_name":"Mathai Mathew P.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"P., Mathai Mathew","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125411452","display_name":"Samkit Jain","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jain, Samkit","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123216388","display_name":"Phani Teja Singamaneni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singamaneni, Phani Teja","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125414449","display_name":"Arun Kumar Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singh, Arun Kumar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125380531","display_name":"K G Ananthu Krishna","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krishna, K. Madhava","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5057814265"],"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.4431000053882599,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.4431000053882599,"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/T10709","display_name":"Social Robot Interaction and HRI","score":0.16820000112056732,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.13019999861717224,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/crowds","display_name":"Crowds","score":0.6787999868392944},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6258999705314636},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6150000095367432},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5483999848365784},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5144000053405762},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.4648999869823456},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4327999949455261},{"id":"https://openalex.org/keywords/human\u2013robot-interaction","display_name":"Human\u2013robot interaction","score":0.41530001163482666}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.694100022315979},{"id":"https://openalex.org/C2777852691","wikidata":"https://www.wikidata.org/wiki/Q13430821","display_name":"Crowds","level":2,"score":0.6787999868392944},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6258999705314636},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6150000095367432},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5685999989509583},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5483999848365784},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5144000053405762},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5081999897956848},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.4648999869823456},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4327999949455261},{"id":"https://openalex.org/C145460709","wikidata":"https://www.wikidata.org/wiki/Q859951","display_name":"Human\u2013robot interaction","level":3,"score":0.41530001163482666},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.3732999861240387},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.3142000138759613},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.301800012588501},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.2994999885559082},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C173246807","wikidata":"https://www.wikidata.org/wiki/Q7833062","display_name":"Trajectory optimization","level":3,"score":0.2718999981880188},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.2662999927997589},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.06698","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.06698","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.06698","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.06698","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Safe":[0],"and":[1,53],"computationally":[2],"efficient":[3],"local":[4],"planning":[5,189],"for":[6,132],"mobile":[7],"robots":[8],"in":[9,37],"dense,":[10],"unstructured":[11],"human":[12,28,38,124],"crowds":[13],"remains":[14],"a":[15,27,46,65,71,79,85,109,117,121,129,168,207],"fundamental":[16],"challenge.":[17],"Moreover,":[18],"ensuring":[19],"that":[20,84,127,158,194],"robot":[21,36,86],"trajectories":[22,76,126,200],"are":[23],"similar":[24],"to":[25,49,77,107,202],"how":[26],"moves":[29],"will":[30],"increase":[31],"the":[32,35,104,133,140,145,148,154,203],"acceptance":[33],"of":[34,73,81,112,123],"environments.":[39],"In":[40],"this":[41],"paper,":[42],"we":[43,63,115,192],"present":[44],"Crowd-FM,":[45],"learning-based":[47,174],"approach":[48,57,152,183],"address":[50],"both":[51],"safety":[52],"human-likeness":[54,130],"challenges.":[55],"Our":[56,151],"has":[58],"two":[59],"novel":[60],"components.":[61],"First,":[62],"train":[64],"Conditional":[66],"Flow-Matching":[67],"(CFM)":[68],"policy":[69,106,161],"over":[70,120],"dataset":[72,122],"optimally":[74],"controlled":[75],"learn":[78,108,116],"set":[80,111],"collision-free":[82,101,165],"primitives":[83],"can":[87,98,163,184,198],"choose":[88],"at":[89],"any":[90],"given":[91],"scenario.":[92],"The":[93],"chosen":[94],"optimal":[95,141],"control":[96],"solver":[97],"generate":[99],"multi-modal":[100],"trajectories,":[102],"allowing":[103],"CFM":[105,160],"diverse":[110],"maneuvers.":[113],"Secondly,":[114],"score":[118,131],"function":[119],"demonstration":[125],"provides":[128],"flow":[134],"primitives.":[135],"At":[136],"inference":[137],"time,":[138],"computing":[139],"trajectory":[142],"requires":[143],"selecting":[144],"one":[146],"with":[147,167,179],"highest":[149],"score.":[150],"improves":[153],"state-of-the-art":[155],"by":[156],"showing":[157],"our":[159,182,195],"alone":[162],"produce":[164],"navigation":[166],"higher":[169],"success":[170],"rate":[171],"than":[172,206],"existing":[173],"baselines.":[175],"Furthermore,":[176],"when":[177],"augmented":[178],"inference-time":[180],"refinement,":[181],"outperform":[185],"even":[186],"expensive":[187],"optimisation-based":[188],"approaches.":[190],"Finally,":[191],"validate":[193],"scoring":[196],"network":[197],"select":[199],"closer":[201],"expert":[204],"data":[205],"manually":[208],"designed":[209],"cost":[210],"function.":[211]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-10T00:00:00"}