{"id":"https://openalex.org/W7133306743","doi":"https://doi.org/10.48550/arxiv.2603.01813","title":"SSMG-Nav: Enhancing Lifelong Object Navigation with Semantic Skeleton Memory Graph","display_name":"SSMG-Nav: Enhancing Lifelong Object Navigation with Semantic Skeleton Memory Graph","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133306743","doi":"https://doi.org/10.48550/arxiv.2603.01813"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01813","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01813","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01813","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024697164","display_name":"Haochen Niu","orcid":"https://orcid.org/0009-0003-6685-0614"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Niu, Haochen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057966068","display_name":"L. Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lantao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076309189","display_name":"Xingwu Ji","orcid":"https://orcid.org/0000-0002-6612-6306"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Xingwu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048965499","display_name":"Rendong Ying","orcid":"https://orcid.org/0000-0001-6670-149X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ying, Rendong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127984446","display_name":"Peilin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Peilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101631903","display_name":"Wen Fei","orcid":"https://orcid.org/0000-0002-1682-4480"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Fei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5024697164"],"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":0.8468000292778015,"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":0.8468000292778015,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.03020000085234642,"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":0.02969999983906746,"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/semantics","display_name":"Semantics (computer science)","score":0.6049000024795532},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.590399980545044},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5674999952316284},{"id":"https://openalex.org/keywords/planner","display_name":"Planner","score":0.5246999859809875},{"id":"https://openalex.org/keywords/path-integration","display_name":"Path integration","score":0.5011000037193298},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4702000021934509},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43639999628067017},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.3840000033378601}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7538999915122986},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6049000024795532},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.590399980545044},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5674999952316284},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.5246999859809875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5120999813079834},{"id":"https://openalex.org/C96522737","wikidata":"https://www.wikidata.org/wiki/Q17148345","display_name":"Path integration","level":2,"score":0.5011000037193298},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4702000021934509},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43639999628067017},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3515999913215637},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3441999852657318},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.34139999747276306},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33169999718666077},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.28130000829696655},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2703000009059906},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01813","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01813","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":"doi:10.48550/arxiv.2603.01813","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01813","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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Navigating":[0],"to":[1,35,99,133,151,177],"out-of-sight":[2],"targets":[3,110],"from":[4,128],"human":[5],"instructions":[6],"in":[7,28],"unfamiliar":[8],"environments":[9],"is":[10],"a":[11,57,64,75,101,117,124,135,153],"core":[12],"capability":[13],"for":[14,59],"service":[15],"robots.":[16],"Despite":[17],"substantial":[18],"progress,":[19],"most":[20],"approaches":[21],"underutilize":[22],"reusable,":[23],"persistent":[24,78],"memory,":[25],"constraining":[26],"performance":[27],"lifelong":[29,168],"settings.":[30],"Many":[31],"are":[32],"additionally":[33],"limited":[34],"single-modality":[36],"inputs":[37],"and":[38,96,113,170,186],"employ":[39],"myopic":[40],"greedy":[41],"policies,":[42],"which":[43],"often":[44],"induce":[45],"inefficient":[46],"back-and-forth":[47],"maneuvers":[48],"(BFMs).":[49],"To":[50,107],"address":[51],"such":[52],"limitations,":[53],"we":[54,115],"introduce":[55],"SSMG-Nav,":[56],"framework":[58],"object":[60],"navigation":[61],"built":[62],"on":[63,166],"\\textit{Semantic":[65],"Skeleton":[66],"Memory":[67],"Graph}":[68],"(SSMG)":[69],"that":[70,156],"consolidates":[71],"past":[72],"observations":[73],"into":[74,92],"spatially":[76],"aligned,":[77],"memory":[79,129],"anchored":[80],"by":[81],"topological":[82],"keypoints":[83],"(e.g.,":[84],"junctions,":[85],"room":[86],"centers).":[87],"SSMG":[88],"clusters":[89],"nearby":[90],"entities":[91],"subgraphs,":[93],"unifying":[94],"entity-":[95],"space-level":[97],"semantics":[98],"yield":[100],"compact":[102],"set":[103],"of":[104,193],"candidate":[105],"destinations.":[106,139],"support":[108],"multimodal":[109,125],"(images,":[111],"objects,":[112],"text),":[114],"integrate":[116],"vision-language":[118],"model":[119],"(VLM).":[120],"For":[121],"each":[122],"subgraph,":[123],"prompt":[126],"synthesized":[127],"guides":[130],"the":[131,191],"VLM":[132],"infer":[134],"target":[136],"belief":[137,147],"over":[138],"A":[140],"long-horizon":[141],"planner":[142],"then":[143],"trades":[144],"off":[145],"this":[146],"against":[148],"traversability":[149],"costs":[150],"produce":[152],"visit":[154],"sequence":[155],"minimizes":[157],"expected":[158],"path":[159,188],"length,":[160],"thereby":[161],"reducing":[162],"backtracking.":[163],"Extensive":[164],"experiments":[165],"challenging":[167],"benchmarks":[169,173],"standard":[171],"ObjectNav":[172],"demonstrate":[174],"that,":[175],"compared":[176],"strong":[178],"baselines,":[179],"our":[180],"method":[181],"achieves":[182],"higher":[183],"success":[184],"rates":[185],"greater":[187],"efficiency,":[189],"validating":[190],"effectiveness":[192],"SSMG-Nav.":[194]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-03-04T00:00:00"}
