{"id":"https://openalex.org/W7134039900","doi":"https://doi.org/10.48550/arxiv.2603.05017","title":"Direct Contact-Tolerant Motion Planning With Vision Language Models","display_name":"Direct Contact-Tolerant Motion Planning With Vision Language Models","publication_year":2026,"publication_date":"2026-03-05","ids":{"openalex":"https://openalex.org/W7134039900","doi":"https://doi.org/10.48550/arxiv.2603.05017"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.05017","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","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/A5128271084","display_name":"He Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, He","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128270726","display_name":"Jian Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128234293","display_name":"Chengyang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chengyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128241715","display_name":"Guoliang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Guoliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075041300","display_name":"Qiyu Ruan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruan, Qiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128254767","display_name":"Shuai Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128250986","display_name":"Chengzhong Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Chengzhong","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28479197,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.4399999976158142,"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":0.4399999976158142,"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":0.39750000834465027,"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":0.04729999974370003,"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/inference","display_name":"Inference","score":0.60589998960495},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5482000112533569},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5040000081062317},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4609000086784363},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.459199994802475},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.45680001378059387},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4309999942779541},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.4196999967098236},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.41769999265670776}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7297999858856201},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7027000188827515},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6814000010490417},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.60589998960495},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5482000112533569},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5040000081062317},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4609000086784363},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.45680001378059387},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.4196999967098236},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.41769999265670776},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.3693000078201294},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C155911833","wikidata":"https://www.wikidata.org/wiki/Q3817354","display_name":"Spatial intelligence","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C2779859299","wikidata":"https://www.wikidata.org/wiki/Q7602527","display_name":"Start point","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.2540999948978424},{"id":"https://openalex.org/C68537008","wikidata":"https://www.wikidata.org/wiki/Q247932","display_name":"Stereopsis","level":2,"score":0.25270000100135803},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.05017","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.05017","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05017","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":"pmh:doi:10.48550/arxiv.2603.05017","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.42186930775642395,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Navigation":[0],"in":[1,35,84,147,163],"cluttered":[2,164],"environments":[3,165],"often":[4],"requires":[5],"robots":[6],"to":[7,15,42,105],"tolerate":[8],"contact":[9],"with":[10,166],"movable":[11,167],"or":[12],"deformable":[13],"objects":[14],"maintain":[16],"efficiency.":[17],"Existing":[18],"contact-tolerant":[19,53],"motion":[20],"planning":[21],"(CTMP)":[22],"methods":[23],"rely":[24],"on":[25],"indirect":[26],"spatial":[27],"representations":[28],"(e.g.,":[29],"prebuilt":[30],"map,":[31],"obstacle":[32],"set),":[33],"resulting":[34],"inaccuracies":[36],"and":[37,65,98,150,160],"a":[38,51,107,123,138,151],"lack":[39],"of":[40],"adaptiveness":[41],"environmental":[43],"uncertainties.":[44],"To":[45],"address":[46],"this":[47],"issue,":[48],"we":[49],"propose":[50],"direct":[52,62,128],"(DCT)":[54],"planner,":[55],"which":[56,80,119,133],"integrates":[57],"vision-language":[58],"models":[59],"(VLMs)":[60],"into":[61],"point":[63,76,109,130],"perception":[64],"navigation,":[66],"including":[67],"two":[68],"key":[69],"components.":[70],"The":[71,111,175],"first":[72],"one":[73],"is":[74,114,134,177],"VLM":[75],"cloud":[77,131],"partitioner":[78],"(VPP),":[79],"performs":[81],"contact-tolerance":[82],"reasoning":[83],"image":[85],"space":[86],"using":[87,96],"VLM,":[88],"caches":[89],"inference":[90],"masks,":[91],"propagates":[92],"them":[93,100],"across":[94,172],"frames":[95],"odometry,":[97],"projects":[99],"onto":[101],"the":[102],"current":[103],"scan":[104],"generate":[106],"contact-aware":[108,129],"cloud.":[110],"second":[112],"innovation":[113],"VPP":[115],"guided":[116],"navigation":[117,162],"(VGN),":[118],"formulates":[120],"CTMP":[121],"as":[122],"perception-to-control":[124],"optimization":[125],"problem":[126],"under":[127],"constraints,":[132],"further":[135],"solved":[136],"by":[137],"specialized":[139],"deep":[140],"neural":[141],"network":[142],"(DNN).":[143],"We":[144],"implement":[145],"DCT":[146,157],"Isaac":[148],"Sim":[149],"real":[152],"car-like":[153],"robot,":[154],"demonstrating":[155],"that":[156],"achieves":[158],"robust":[159],"efficient":[161],"obstacles,":[168],"outperforming":[169],"representative":[170],"baselines":[171],"diverse":[173],"metrics.":[174],"code":[176],"available":[178],"at:":[179],"https://github.com/ChrisLeeUM/DCT.":[180]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-03-07T00:00:00"}