{"id":"https://openalex.org/W7164836646","doi":"https://doi.org/10.48550/arxiv.2606.13883","title":"Guided Diffusion with Distilled Vision-Language Reliability for Aerial Navigation","display_name":"Guided Diffusion with Distilled Vision-Language Reliability for Aerial Navigation","publication_year":2026,"publication_date":"2026-06-11","ids":{"openalex":"https://openalex.org/W7164836646","doi":"https://doi.org/10.48550/arxiv.2606.13883"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.13883","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13883","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.2606.13883","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128872561","display_name":"Ivan Valuev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Valuev, Ivan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138656839","display_name":"Iana Zhura","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhura, Iana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065937697","display_name":"Valerii Serpiva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Serpiva, Valerii","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138655226","display_name":"Didar Seyidov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seyidov, Didar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138688093","display_name":"Dzmitry Tsetserukou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tsetserukou, Dzmitry","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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.4027999937534332,"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":0.4027999937534332,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.367900013923645,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.053199999034404755,"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/reliability","display_name":"Reliability (semiconductor)","score":0.7300999760627747},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5600000023841858},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4602999985218048},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4438000023365021},{"id":"https://openalex.org/keywords/planner","display_name":"Planner","score":0.4162999987602234},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.3889000117778778},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.3555000126361847},{"id":"https://openalex.org/keywords/failure-rate","display_name":"Failure rate","score":0.3488999903202057}],"concepts":[{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.7300999760627747},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.612500011920929},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5600000023841858},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4943999946117401},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.46369999647140503},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4602999985218048},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4438000023365021},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43529999256134033},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3889000117778778},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3555000126361847},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3513999879360199},{"id":"https://openalex.org/C163164238","wikidata":"https://www.wikidata.org/wiki/Q2737027","display_name":"Failure rate","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3384000062942505},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C2779733308","wikidata":"https://www.wikidata.org/wiki/Q17146464","display_name":"Tripping","level":3,"score":0.28780001401901245},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2806999981403351},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C201729545","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability theory","level":3,"score":0.2734000086784363},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C2776654903","wikidata":"https://www.wikidata.org/wiki/Q2601463","display_name":"SAFER","level":2,"score":0.2685999870300293}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.13883","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13883","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.2606.13883","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13883","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":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4592270255088806}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Autonomous":[0],"UAV":[1,81],"navigation":[2],"is":[3,55],"conventionally":[4],"solved":[5],"by":[6,32,104],"pipelines":[7],"that":[8,96,108,140],"separate":[9],"perception,":[10],"mapping,":[11],"and":[12,22,57,65,146,158,182],"planning":[13,120],"into":[14],"distinct":[15],"stages,":[16],"which":[17],"propagates":[18],"errors,":[19],"accumulates":[20],"latency,":[21],"requires":[23],"environment-specific":[24],"retuning.":[25],"End-to-end":[26],"generative":[27],"models":[28],"remove":[29],"these":[30],"interfaces":[31],"mapping":[33],"raw":[34],"observations":[35],"directly":[36],"to":[37,124,180,192,204,218],"trajectories,":[38],"but":[39],"inherit":[40],"a":[41,75,92,105,114,135,160,170],"subtle":[42],"failure":[43],"mode:":[44],"trained":[45],"on":[46,87,153,159],"clean":[47],"data,":[48],"they":[49],"cannot":[50,100],"recognise":[51],"when":[52],"an":[53],"observation":[54,89],"unreliable,":[56],"treat":[58],"degraded":[59],"regions":[60,152,189],"such":[61],"as":[62,68,208],"glass,":[63],"mirrors,":[64],"overexposed":[66],"surfaces":[67],"valid":[69],"evidence":[70],"for":[71,79],"planning.":[72],"We":[73],"present":[74],"reliability-aware":[76],"diffusion":[77,172],"planner":[78,164],"3D":[80],"navigation.":[82],"It":[83],"conditions":[84],"trajectory":[85],"generation":[86],"the":[88,110,118,131,175,184,195,209,215,223],"together":[90],"with":[91,134],"scene-level":[93],"reliability":[94,186,196,201],"heatmap":[95],"marks":[97],"where":[98],"perception":[99],"be":[101],"trusted,":[102],"produced":[103],"lightweight":[106],"network":[107],"distils":[109],"open-vocabulary":[111],"reasoning":[112],"of":[113,187],"vision-language":[115,225],"model":[116],"within":[117],"real-time":[119],"budget.":[121],"To":[122],"generalise":[123],"unseen":[125],"environments":[126],"without":[127],"retraining,":[128],"we":[129],"steer":[130],"denoising":[132],"process":[133],"differentiable":[136],"two-stage":[137],"ESDF":[138],"cost":[139],"treats":[141],"physical":[142],"obstacles":[143,148],"from":[144,149,178,190,202],"depth":[145],"virtual":[147],"highly":[150],"unreliable":[151],"equal":[154],"footing.":[155],"In":[156],"simulation":[157],"real":[161],"quadrotor,":[162],"our":[163],"produces":[165],"markedly":[166],"safer":[167],"trajectories":[168],"than":[169,222],"state-of-the-art":[171],"baseline,":[173],"reducing":[174],"obstacle-violation":[176],"rate":[177],"40.3%":[179],"9.6%":[181],"raising":[183],"mean":[185,200],"traversed":[188],"0.588":[191],"0.925.":[193],"Ablating":[194],"term":[197],"alone":[198],"drops":[199],"0.898":[203],"0.783,":[205],"confirming":[206],"it":[207],"decisive":[210],"component,":[211],"while":[212],"distillation":[213],"runs":[214],"framework":[216],"up":[217],"2":[219],"times":[220],"faster":[221],"full":[224],"model.":[226]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-16T00:00:00"}
