{"id":"https://openalex.org/W4416751067","doi":"https://doi.org/10.1109/iros60139.2025.11246282","title":"PartGrasp: Generalizable Part-level Grasping via Semantic-Geometric Alignment","display_name":"PartGrasp: Generalizable Part-level Grasping via Semantic-Geometric Alignment","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416751067","doi":"https://doi.org/10.1109/iros60139.2025.11246282"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11246282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075189189","display_name":"Haoyang Lu","orcid":"https://orcid.org/0000-0002-7429-4035"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyang Lu","raw_affiliation_strings":["Beijing Institute of Technology,School of Automation,Beijing,China,100081"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Automation,Beijing,China,100081","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086107358","display_name":"Chengcai Yang","orcid":"https://orcid.org/0000-0003-3078-6449"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengcai Yang","raw_affiliation_strings":["Beijing Institute of Technology,School of Automation,Beijing,China,100081"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Automation,Beijing,China,100081","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050719461","display_name":"Guangyan Chen","orcid":"https://orcid.org/0000-0002-2903-1957"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangyan Chen","raw_affiliation_strings":["Beijing Institute of Technology,School of Automation,Beijing,China,100081"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Automation,Beijing,China,100081","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068949782","display_name":"Yufeng Yue","orcid":"https://orcid.org/0000-0001-6628-7946"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufeng Yue","raw_affiliation_strings":["Beijing Institute of Technology,School of Automation,Beijing,China,100081"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Automation,Beijing,China,100081","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"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":"21273","last_page":"21280"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10868","display_name":"Soft Robotics and Applications","score":0.0010999999940395355,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.000699999975040555,"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/grasp","display_name":"GRASP","score":0.9340000152587891},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7580999732017517},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6664000153541565},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5478000044822693},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3276999890804291},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.3199999928474426}],"concepts":[{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.9340000152587891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7609999775886536},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7580999732017517},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7307999730110168},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6664000153541565},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.560699999332428},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5478000044822693},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.3199999928474426},{"id":"https://openalex.org/C31487907","wikidata":"https://www.wikidata.org/wiki/Q1154597","display_name":"Polygon mesh","level":2,"score":0.3174999952316284},{"id":"https://openalex.org/C3004257","wikidata":"https://www.wikidata.org/wiki/Q17084606","display_name":"Correspondence problem","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.30239999294281006},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C2988191880","wikidata":"https://www.wikidata.org/wiki/Q40687","display_name":"Robotic hand","level":3,"score":0.2793000042438507},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26350000500679016}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11246282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1913744585","https://openalex.org/W1991544872","https://openalex.org/W2049981393","https://openalex.org/W2128019145","https://openalex.org/W2150382645","https://openalex.org/W2600030077","https://openalex.org/W2986303149","https://openalex.org/W3035198432","https://openalex.org/W3159481202","https://openalex.org/W4312933868","https://openalex.org/W4382366145","https://openalex.org/W4383097638","https://openalex.org/W4386065846","https://openalex.org/W4386066287","https://openalex.org/W4386072455","https://openalex.org/W4390872744","https://openalex.org/W4391377094","https://openalex.org/W4400904825","https://openalex.org/W4401414814","https://openalex.org/W4402354127","https://openalex.org/W4402753712","https://openalex.org/W4402753726","https://openalex.org/W4403842181","https://openalex.org/W4404446892","https://openalex.org/W4405785944","https://openalex.org/W4413146851","https://openalex.org/W4413925017","https://openalex.org/W4413925353","https://openalex.org/W4415797243","https://openalex.org/W4415800665"],"related_works":[],"abstract_inverted_index":{"The":[0],"ability":[1],"to":[2,33,48,129,143],"perform":[3,115],"generalizable":[4,82],"and":[5,42,60,85,101,146,163,166],"precise":[6,50,74,86],"grasping":[7,76,139],"on":[8,171],"functional":[9],"object":[10,102],"parts":[11,36],"is":[12,141],"a":[13,70,93,108,116,124],"prerequisite":[14],"for":[15],"robotic":[16],"manipulation":[17],"in":[18,30,157],"open":[19],"environments.":[20],"Recent":[21],"foundation":[22],"models":[23],"have":[24],"demonstrated":[25],"promising":[26],"semantic":[27,83,120],"correspondence":[28,84],"capabilities":[29],"guiding":[31],"robots":[32],"grasp":[34,51,94,99],"similar":[35],"across":[37],"objects":[38,56],"with":[39],"resembling":[40],"shapes":[41],"poses.":[43],"However,":[44],"existing":[45],"works":[46],"struggle":[47],"generalize":[49],"poses":[52,100],"when":[53],"the":[54,152],"target":[55],"exhibit":[57],"substantial":[58],"geometric":[59,87,130],"positional":[61],"variations.":[62,131,148],"To":[63],"tackle":[64],"this":[65,111],"challenge,":[66],"we":[67,90,113],"present":[68],"PartGrasp,":[69],"method":[71,156],"that":[72,127,140],"achieves":[73,134],"part":[75,138],"through":[77],"hierarchical":[78],"integration":[79],"of":[80,137,154,159],"highly":[81],"registration.":[88],"Specifically,":[89],"first":[91],"build":[92],"knowledge":[95],"bank":[96],"by":[97,123],"extracting":[98],"meshes":[103],"from":[104,110],"demonstrations.":[105],"Upon":[106],"retrieving":[107],"reference":[109],"bank,":[112],"initially":[114],"coarse":[117],"alignment":[118],"using":[119],"correspondence,":[121],"followed":[122],"fine":[125],"registration":[126],"adapts":[128],"This":[132],"approach":[133],"fine-grained":[135],"generalization":[136,161],"robust":[142],"both":[144,160],"shape":[145],"pose":[147],"Extensive":[149],"experiments":[150],"demonstrate":[151],"efficacy":[153],"our":[155,172],"terms":[158],"capability":[162],"accuracy.":[164],"Videos":[165],"more":[167],"details":[168],"are":[169],"available":[170],"project":[173],"site:":[174],"https://part-grasp.github.io/partgrasp/.":[175]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-28T00:00:00"}