{"id":"https://openalex.org/W7164002592","doi":"https://doi.org/10.48550/arxiv.2606.09311","title":"FF-JEPA: Long-Horizon Planning in World Models with Latent Planners","display_name":"FF-JEPA: Long-Horizon Planning in World Models with Latent Planners","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7164002592","doi":"https://doi.org/10.48550/arxiv.2606.09311"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.09311","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09311","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":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.2606.09311","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008219599","display_name":"Sergi Masip","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Masip, Sergi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138284392","display_name":"Jonathan Swinnen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Swinnen, Jonathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138248516","display_name":"Yutong Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yutong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138254725","display_name":"Renaud Detry","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Detry, Renaud","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138263901","display_name":"Tinne Tuytelaars","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tuytelaars, Tinne","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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.5303999781608582,"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"}},"topics":[{"id":"https://openalex.org/T10906","display_name":"AI-based Problem Solving and Planning","score":0.5303999781608582,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.13249999284744263,"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":0.06620000302791595,"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/embedding","display_name":"Embedding","score":0.6603000164031982},{"id":"https://openalex.org/keywords/planner","display_name":"Planner","score":0.5295000076293945},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5005999803543091},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4729999899864197},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.46320000290870667},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.376800000667572}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6725999712944031},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6603000164031982},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.5295000076293945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5023999810218811},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5005999803543091},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4729999899864197},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.46320000290870667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39809998869895935},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.3424000144004822},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C114073186","wikidata":"https://www.wikidata.org/wiki/Q2631895","display_name":"Automated planning and scheduling","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25870001316070557},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.09311","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09311","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":"doi:10.48550/arxiv.2606.09311","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09311","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.7063102126121521,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Joint":[0],"Embedding":[1],"Predictive":[2],"Architectures":[3],"(JEPAs)":[4],"have":[5],"shown":[6],"promising":[7,142],"world":[8,133],"modeling":[9],"capabilities,":[10],"enabling":[11],"planning":[12,110],"in":[13,56],"latent":[14,88],"space":[15],"by":[16,66,111],"optimizing":[17],"action":[18],"trajectories":[19,114],"using":[20],"methods":[21,28,41],"like":[22],"the":[23,48,92,96,102],"Cross-Entropy":[24],"Method":[25],"(CEM).":[26],"These":[27],"are,":[29],"however,":[30],"too":[31],"computationally":[32],"expensive":[33],"and":[34,107],"ineffective":[35],"for":[36,104,144],"long-horizon":[37,109,135],"planning.":[38,146],"Furthermore,":[39],"these":[40,64],"typically":[42],"require":[43],"an":[44,86],"explicit":[45],"image":[46],"of":[47,118],"goal":[49,105],"state,":[50],"which":[51],"is":[52],"not":[53],"always":[54],"possible":[55],"real-world":[57],"tasks.":[58],"In":[59],"this":[60,138],"work,":[61],"we":[62,84],"tackle":[63],"limitations":[65],"proposing":[67],"Forward-Forward-JEPA":[68],"(FF-JEPA),":[69],"a":[70,79,116,141],"hierarchical":[71],"approach":[72,100,139],"leveraging":[73],"two":[74],"forward":[75,82],"dynamics":[76],"models.":[77],"Alongside":[78],"standard":[80],"action-conditioned":[81],"model,":[83],"introduce":[85],"action-free":[87],"planner":[89],"that":[90,128],"predicts":[91],"next":[93],"subgoal":[94],"given":[95],"current":[97],"state.":[98],"This":[99],"removes":[101],"need":[103],"images":[106],"enables":[108],"decomposing":[112],"complex":[113],"into":[115],"sequence":[117],"tractable,":[119],"short-term":[120],"optimization":[121],"problems.":[122],"Preliminary":[123],"results":[124],"on":[125],"PushT":[126],"demonstrate":[127],"FF-JEPA":[129],"successfully":[130],"overcomes":[131],"flat":[132],"models'":[134],"collapse,":[136],"highlighting":[137],"as":[140],"direction":[143],"goal-free":[145]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}
