{"id":"https://openalex.org/W7168264588","doi":"https://doi.org/10.48550/arxiv.2607.10744","title":"Traj-VLN: Learning Pixel-Space Interaction via Autoregressive Trajectory Generation","display_name":"Traj-VLN: Learning Pixel-Space Interaction via Autoregressive Trajectory Generation","publication_year":2026,"publication_date":"2026-07-12","ids":{"openalex":"https://openalex.org/W7168264588","doi":"https://doi.org/10.48550/arxiv.2607.10744"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.10744","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10744","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.2607.10744","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140713514","display_name":"Changfei Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Changfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140646771","display_name":"Guangcheng Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Guangcheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140582510","display_name":"Wenjun Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Aoxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140671009","display_name":"Hong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Haoxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5146609466","display_name":"Wenjun Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Wenjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5146782958","display_name":"Hong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Hong","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":0.9846000075340271,"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":0.9846000075340271,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.00139999995008111,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.00139999995008111,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7150999903678894},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7020999789237976},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5273000001907349},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5163000226020813},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.4571000039577484},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.41909998655319214},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4187000095844269},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4178999960422516}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7150999903678894},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7020999789237976},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6953999996185303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6013000011444092},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5273000001907349},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5163000226020813},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.4571000039577484},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4489000141620636},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.41909998655319214},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4187000095844269},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4178999960422516},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C100609095","wikidata":"https://www.wikidata.org/wiki/Q1335050","display_name":"Embodied cognition","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3109999895095825},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2754000127315521},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2538999915122986},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.10744","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10744","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.2607.10744","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10744","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":0.7355631589889526,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Benefiting":[0],"from":[1,197],"the":[2,11,34,53,100,109,112,124,127,198,202,223],"powerful":[3],"priors":[4],"embedded":[5],"in":[6,25,52,58,170],"large-scale":[7],"pre-training":[8],"data":[9],"and":[10,44,114,182,248],"emerging":[12],"commonsense":[13],"reasoning":[14],"ability,":[15],"large":[16],"language":[17,35],"models":[18,39],"(LLMs)":[19],"have":[20],"shown":[21],"unprecedented":[22],"generalization":[23,46],"capabilities":[24],"many":[26],"research":[27],"fields.":[28],"Recently,":[29],"projecting":[30],"visual":[31],"embeddings":[32],"into":[33,85,123],"space":[36,173],"via":[37],"vision-language":[38],"(VLMs)":[40],"to":[41,67,92,108,165],"achieve":[42],"sim-toreal":[43],"cross-scene":[45],"has":[47,208],"become":[48],"a":[49,79,86,93,179,189,195],"prevailing":[50],"paradigm":[51],"field":[54],"of":[55,88,95,111,129,191,201,215,225],"Vision-and-Language":[56],"Navigation":[57],"Continuous":[59],"Environments":[60],"(VLN-CE).":[61],"VLN":[62,80,230],"requires":[63],"an":[64,160],"embodied":[65],"agent":[66],"navigate":[68],"through":[69,174],"unseen":[70],"environments":[71,101],"following":[72],"natural":[73],"linguistic":[74,180],"instructions.":[75],"We":[76],"emphasize":[77],"that":[78,210,222,235],"task":[81],"can":[82],"be":[83],"decomposed":[84],"sequence":[87],"sub-tasks,":[89],"each":[90],"corresponding":[91],"process":[94],"3D":[96,151],"spatial":[97,119],"interaction":[98],"with":[99,143,243],"described":[102],"by":[103],"instructions":[104],"such":[105,118],"as":[106,136],"\"walk":[107],"end":[110],"sofa":[113],"turn":[115],"left.\"":[116],"However,":[117],"interactions":[120,168],"involving":[121],"moving":[122],"image":[125],"along":[126],"direction":[128],"depth":[130,149],"sensing":[131],"are":[132],"puzzling":[133],"for":[134],"VLMs":[135,154,164],"they":[137],"were":[138],"predominantly":[139],"trained":[140],"on":[141],"conversations":[142],"RGB":[144],"images.":[145],"Rather":[146],"than":[147],"incorporating":[148],"or":[150],"geometric":[152],"information-which":[153],"rarely":[155],"encounter":[156],"during":[157],"pretrainingwe":[158],"propose":[159],"alternative":[161],"approach:":[162],"fine-tuning":[163],"learn":[166],"navigation":[167],"directly":[169],"2D":[171],"pixel":[172,192],"autoregressive":[175],"trajectory":[176,196,227],"generation.":[177],"Given":[178],"instruction":[181],"historical":[183],"observations,":[184],"our":[185,218,236],"model":[186,238],"sequentially":[187],"predicts":[188],"series":[190],"coordinates,":[193],"drawing":[194],"bottom":[199],"center":[200],"current":[203],"observation.":[204],"While":[205],"prior":[206],"work":[207],"proved":[209],"pixel-goal":[211],"supervision":[212,224],"outperforms":[213],"learning":[214],"discrete":[216],"actions,":[217],"experiments":[219],"further":[220],"verify":[221],"pixel-space":[226],"significantly":[228],"enhances":[229],"performance.":[231],"Moreover,":[232],"we":[233],"demonstrate":[234],"flagship":[237],"achieves":[239],"state-of-the-art":[240],"level":[241],"performance":[242],"relatively":[244],"limited":[245],"computational":[246],"resources":[247],"training":[249],"data.":[250]},"counts_by_year":[],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2026-07-15T00:00:00"}