{"id":"https://openalex.org/W2964239605","doi":"https://doi.org/10.1109/icra.2018.8460887","title":"Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning","display_name":"Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2964239605","doi":"https://doi.org/10.1109/icra.2018.8460887","mag":"2964239605"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2018.8460887","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8460887","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"article","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/A5020901032","display_name":"Jeffrey Mahler","orcid":"https://orcid.org/0000-0003-3204-3024"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jeffrey Mahler","raw_affiliation_strings":["Dept. of Electrical Engineering and Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering and Computer Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090422109","display_name":"Matthew Matl","orcid":"https://orcid.org/0000-0003-0173-2820"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matthew Matl","raw_affiliation_strings":["Dept. of Electrical Engineering and Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering and Computer Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100446460","display_name":"Xinyu Liu","orcid":"https://orcid.org/0009-0004-6776-1821"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinyu Liu","raw_affiliation_strings":["Dept. of Electrical Engineering and Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering and Computer Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404000","display_name":"Albert P. Li","orcid":"https://orcid.org/0000-0001-5772-6265"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Albert Li","raw_affiliation_strings":["Dept. of Electrical Engineering and Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering and Computer Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089497670","display_name":"David V. Gealy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David Gealy","raw_affiliation_strings":["Dept. of Electrical Engineering and Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering and Computer Science","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050342525","display_name":"Ken Goldberg","orcid":"https://orcid.org/0000-0002-2661-4524"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ken Goldberg","raw_affiliation_strings":["Dept. of Industrial Operations and Engineering Research, AUTOLAB University of California, Berkeley, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Industrial Operations and Engineering Research, AUTOLAB University of California, Berkeley, USA","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5020901032"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":27.3455,"has_fulltext":false,"cited_by_count":326,"citation_normalized_percentile":{"value":0.99811842,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"5620","last_page":"5627"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9987999796867371,"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":0.9987999796867371,"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":0.949400007724762,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.9163607358932495},{"id":"https://openalex.org/keywords/wrench","display_name":"Wrench","score":0.8159939050674438},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7329807877540588},{"id":"https://openalex.org/keywords/grippers","display_name":"Grippers","score":0.7076354026794434},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6489051580429077},{"id":"https://openalex.org/keywords/lift","display_name":"Lift (data mining)","score":0.5649122595787048},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.551676332950592},{"id":"https://openalex.org/keywords/suction-cup","display_name":"Suction cup","score":0.4961467683315277},{"id":"https://openalex.org/keywords/suction","display_name":"Suction","score":0.4961467683315277},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4917929470539093},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.477640300989151},{"id":"https://openalex.org/keywords/clipping","display_name":"Clipping (morphology)","score":0.4162522256374359},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4139874577522278},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.24481478333473206},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20997834205627441},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.15768033266067505}],"concepts":[{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.9163607358932495},{"id":"https://openalex.org/C29302406","wikidata":"https://www.wikidata.org/wiki/Q154411","display_name":"Wrench","level":2,"score":0.8159939050674438},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7329807877540588},{"id":"https://openalex.org/C2775960376","wikidata":"https://www.wikidata.org/wiki/Q1435859","display_name":"Grippers","level":2,"score":0.7076354026794434},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6489051580429077},{"id":"https://openalex.org/C139002025","wikidata":"https://www.wikidata.org/wiki/Q3001212","display_name":"Lift (data mining)","level":2,"score":0.5649122595787048},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.551676332950592},{"id":"https://openalex.org/C87321777","wikidata":"https://www.wikidata.org/wiki/Q635971","display_name":"Suction cup","level":2,"score":0.4961467683315277},{"id":"https://openalex.org/C23795335","wikidata":"https://www.wikidata.org/wiki/Q1790295","display_name":"Suction","level":2,"score":0.4961467683315277},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4917929470539093},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.477640300989151},{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.4162522256374359},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4139874577522278},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.24481478333473206},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20997834205627441},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.15768033266067505},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2018.8460887","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8460887","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W603908379","https://openalex.org/W1495615928","https://openalex.org/W1503925285","https://openalex.org/W1540269160","https://openalex.org/W1564897360","https://openalex.org/W1794703952","https://openalex.org/W1985551004","https://openalex.org/W1992989752","https://openalex.org/W1999156278","https://openalex.org/W2021683594","https://openalex.org/W2033819227","https://openalex.org/W2041376653","https://openalex.org/W2046062329","https://openalex.org/W2050450713","https://openalex.org/W2076363786","https://openalex.org/W2086983719","https://openalex.org/W2088043683","https://openalex.org/W2096780166","https://openalex.org/W2124075156","https://openalex.org/W2153400869","https://openalex.org/W2170940415","https://openalex.org/W2201912979","https://openalex.org/W2212932033","https://openalex.org/W2341294985","https://openalex.org/W2414685554","https://openalex.org/W2466591533","https://openalex.org/W2485911221","https://openalex.org/W2529170537","https://openalex.org/W2533256670","https://openalex.org/W2600030077","https://openalex.org/W2624940943","https://openalex.org/W2737979835","https://openalex.org/W2962736495","https://openalex.org/W2962746398","https://openalex.org/W2963394817","https://openalex.org/W4255298361","https://openalex.org/W6618372016","https://openalex.org/W6630226614","https://openalex.org/W6638325340","https://openalex.org/W6655864451","https://openalex.org/W6687808982","https://openalex.org/W6697071109","https://openalex.org/W6704253388","https://openalex.org/W6715805826","https://openalex.org/W6721937913","https://openalex.org/W6728043242","https://openalex.org/W6728186987","https://openalex.org/W6739233934","https://openalex.org/W6744002472","https://openalex.org/W6744542307","https://openalex.org/W6907258182"],"related_works":["https://openalex.org/W2947091406","https://openalex.org/W2069698258","https://openalex.org/W4312555732","https://openalex.org/W2095492957","https://openalex.org/W2892546005","https://openalex.org/W2567010246","https://openalex.org/W2745924412","https://openalex.org/W4237408708","https://openalex.org/W2052350261","https://openalex.org/W2922139741"],"abstract_inverted_index":{"Vacuum-based":[0],"end":[1],"effectors":[2],"are":[3,9],"widely":[4],"used":[5],"in":[6,96,149,161,213],"industry":[7],"and":[8,14,68,72,98,103,127,192,207,227],"often":[10,33],"preferred":[11],"over":[12],"parallel-jaw":[13],"multifinger":[15],"grippers":[16],"due":[17],"to":[18,21,82,94,114,135,144,211],"their":[19],"ability":[20,77],"lift":[22],"objects":[23,179],"with":[24,170],"a":[25,52,73,118,137,153,171],"single":[26,154],"point":[27,38,123,150],"of":[28,44,61,75,78,120,204],"contact.":[29],"Suction":[30],"grasp":[31,81,128],"planners":[32],"target":[34,70],"planar":[35],"surfaces":[36],"on":[37,165,177],"clouds":[39,151],"near":[40],"the":[41,59,62,65,76,79,158,214,218],"estimated":[42],"centroid":[43],"an":[45,84,166],"object.":[46,155],"In":[47],"this":[48],"paper,":[49],"we":[50,91,181],"propose":[51],"compliant":[53],"suction":[54,66,80,125,147,173],"contact":[55],"model":[56],"that":[57,180],"computes":[58],"quality":[60],"seal":[63],"between":[64],"cup":[67],"local":[69],"surface":[71],"measure":[74],"resist":[83],"external":[85,104],"gravity":[86],"wrench.":[87],"To":[88],"characterize":[89],"grasps,":[90,126],"estimate":[92],"robustness":[93,129],"perturbations":[95],"end-effector":[97],"object":[99,112],"pose,":[100],"material":[101,229],"properties,":[102],"wrenches.":[105],"We":[106,131,156],"analyze":[107],"grasps":[108],"across":[109],"1,500":[110],"3D":[111],"models":[113],"generate":[115],"Dex-Net":[116,133,199],"3.0,":[117],"dataset":[119],"2.8":[121],"million":[122],"clouds,":[124],"labels.":[130],"use":[132],"3.0":[134,200],"train":[136],"Grasp":[138],"Quality":[139],"Convolutional":[140],"Neural":[141],"Network":[142],"(GQ-CNN)":[143],"classify":[145],"robust":[146],"targets":[148],"containing":[152],"evaluate":[157],"resulting":[159],"system":[160],"350":[162],"physical":[163],"trials":[164],"ABB":[167],"YuMi":[168],"fitted":[169],"pneumatic":[172],"gripper.":[174],"When":[175],"evaluated":[176],"novel":[178],"categorize":[182],"as":[183],"Basic":[184],"(prismatic":[185],"or":[186],"cylindrical),":[187],"Typical":[188],"(more":[189],"complex":[190],"geometry),":[191],"Adversarial":[193],"(with":[194],"few":[195],"available":[196],"suction-grasp":[197],"points)":[198],"achieves":[201],"success":[202],"rates":[203],"98%,":[205],"82%,":[206],"58%":[208],"respectively,":[209],"improving":[210],"81%":[212],"latter":[215],"case":[216],"when":[217],"training":[219],"set":[220],"includes":[221],"only":[222],"adversarial":[223],"objects.":[224],"Code,":[225],"datasets,":[226],"supplemental":[228],"can":[230],"be":[231],"found":[232],"at":[233],"http://berkeleyautomation.github.io/dex-net.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":41},{"year":2024,"cited_by_count":36},{"year":2023,"cited_by_count":46},{"year":2022,"cited_by_count":51},{"year":2021,"cited_by_count":46},{"year":2020,"cited_by_count":42},{"year":2019,"cited_by_count":43},{"year":2018,"cited_by_count":15},{"year":2017,"cited_by_count":2}],"updated_date":"2026-05-29T09:21:14.243279","created_date":"2025-10-10T00:00:00"}