{"id":"https://openalex.org/W3214907063","doi":"https://doi.org/10.1109/iros51168.2021.9636777","title":"Multi-Object Grasping \u2013 Estimating the Number of Objects in a Robotic Grasp","display_name":"Multi-Object Grasping \u2013 Estimating the Number of Objects in a Robotic Grasp","publication_year":2021,"publication_date":"2021-09-27","ids":{"openalex":"https://openalex.org/W3214907063","doi":"https://doi.org/10.1109/iros51168.2021.9636777","mag":"3214907063"},"language":"en","primary_location":{"id":"doi:10.1109/iros51168.2021.9636777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros51168.2021.9636777","pdf_url":null,"source":{"id":"https://openalex.org/S4363607734","display_name":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5046826993","display_name":"Tianze Chen","orcid":"https://orcid.org/0009-0004-9016-5294"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianze Chen","raw_affiliation_strings":["University of South Florida, Tampa, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021294041","display_name":"Adheesh Shenoy","orcid":null},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adheesh Shenoy","raw_affiliation_strings":["University of South Florida, Tampa, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056204951","display_name":"Anzhelika Kolinko","orcid":null},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anzhelika Kolinko","raw_affiliation_strings":["University of South Florida, Tampa, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018192925","display_name":"Syed Tafseer Haider Shah","orcid":null},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Syed Shah","raw_affiliation_strings":["University of South Florida, Tampa, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100772261","display_name":"Yu Sun","orcid":"https://orcid.org/0000-0001-9953-291X"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Sun","raw_affiliation_strings":["University of South Florida, Tampa, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.9654,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.98768511,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4995","last_page":"5001"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9998999834060669,"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":0.9998999834060669,"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/T10784","display_name":"Muscle activation and electromyography studies","score":0.996399998664856,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9779000282287598,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.986319899559021},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6912816762924194},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6737530827522278},{"id":"https://openalex.org/keywords/grippers","display_name":"Grippers","score":0.6524055600166321},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6264016032218933},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6068925857543945},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5429002046585083},{"id":"https://openalex.org/keywords/tactile-sensor","display_name":"Tactile sensor","score":0.5411853194236755},{"id":"https://openalex.org/keywords/robotic-hand","display_name":"Robotic hand","score":0.5104085803031921},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.47850313782691956},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2312030792236328},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.06197178363800049}],"concepts":[{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.986319899559021},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6912816762924194},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6737530827522278},{"id":"https://openalex.org/C2775960376","wikidata":"https://www.wikidata.org/wiki/Q1435859","display_name":"Grippers","level":2,"score":0.6524055600166321},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6264016032218933},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6068925857543945},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5429002046585083},{"id":"https://openalex.org/C46722567","wikidata":"https://www.wikidata.org/wiki/Q7674139","display_name":"Tactile sensor","level":3,"score":0.5411853194236755},{"id":"https://openalex.org/C2988191880","wikidata":"https://www.wikidata.org/wiki/Q40687","display_name":"Robotic hand","level":3,"score":0.5104085803031921},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.47850313782691956},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2312030792236328},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.06197178363800049},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros51168.2021.9636777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros51168.2021.9636777","pdf_url":null,"source":{"id":"https://openalex.org/S4363607734","display_name":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1581953684","https://openalex.org/W1973903076","https://openalex.org/W1979709633","https://openalex.org/W1979814594","https://openalex.org/W2021570254","https://openalex.org/W2023509699","https://openalex.org/W2035557456","https://openalex.org/W2041350515","https://openalex.org/W2048076085","https://openalex.org/W2062730441","https://openalex.org/W2087430694","https://openalex.org/W2106828303","https://openalex.org/W2112714380","https://openalex.org/W2129190609","https://openalex.org/W2129296354","https://openalex.org/W2138351865","https://openalex.org/W2141789091","https://openalex.org/W2146832880","https://openalex.org/W2149606722","https://openalex.org/W2155289806","https://openalex.org/W2161103583","https://openalex.org/W2166459342","https://openalex.org/W2219151122","https://openalex.org/W2460420135","https://openalex.org/W2472910955","https://openalex.org/W2545671521","https://openalex.org/W2549961547","https://openalex.org/W2737569089","https://openalex.org/W2771515333","https://openalex.org/W2783347051","https://openalex.org/W2811690862","https://openalex.org/W2963263790","https://openalex.org/W2964041256","https://openalex.org/W3021269843","https://openalex.org/W3089733074","https://openalex.org/W3101594387","https://openalex.org/W3216908904","https://openalex.org/W4238299461","https://openalex.org/W6719273843"],"related_works":["https://openalex.org/W2583647647","https://openalex.org/W4312985843","https://openalex.org/W2613999385","https://openalex.org/W3133406196","https://openalex.org/W4210583734","https://openalex.org/W2544373263","https://openalex.org/W2166446064","https://openalex.org/W3159512259","https://openalex.org/W4386100501","https://openalex.org/W2968390578"],"abstract_inverted_index":{"A":[0,116],"human":[1],"hand":[2,67,90,118,169,191,216],"can":[3,201],"grasp":[4,29,40,51,78,97,127,149],"a":[5,13,23,28,31,56,126,148,156,167,176,180,203],"desired":[6],"number":[7,35],"of":[8,36,113],"objects":[9,37,75,87,108,123,194,211],"at":[10],"once":[11,188],"from":[12],"pile":[14,72,111],"based":[15],"solely":[16],"on":[17,166,208],"tactile":[18,130,152],"sensing.":[19],"To":[20],"do":[21],"so,":[22],"robot":[24],"needs":[25],"to":[26,82,142],"make":[27,202],"in":[30,38,49,70,76,88,109,125,173,195,214,230,240],"pile,":[32,197],"sense":[33,120],"the":[34,39,50,63,65,71,74,77,89,96,99,110,114,189,196,198,215,241],"before":[41,91,132,206],"lifting,":[42],"and":[43,73,155,170,175,226,232,236],"predict":[44],"how":[45,121,209],"many":[46,122,210],"will":[47,212],"remain":[48,213],"after":[52,217],"lifting.":[53,133,218],"It":[54],"is":[55,68],"very":[57],"challenging":[58],"problem":[59],"because":[60,102],"when":[61,98],"making":[62],"prediction,":[64],"robotic":[66,117,181],"still":[69],"are":[79,124,222],"not":[80],"observable":[81],"vision":[83],"systems.":[84],"Moreover,":[85],"some":[86],"lifting":[92,100,207],"may":[93],"fall":[94],"out":[95],"starts":[101],"they":[103],"were":[104],"supported":[105],"by":[106],"other":[107],"instead":[112],"fingers.":[115],"should":[119],"using":[128],"its":[129],"sensors":[131],"This":[134],"paper":[135],"presents":[136],"novel":[137],"multi-object":[138],"grasping":[139],"analyzing":[140],"methods":[141,162],"solve":[143],"this":[144],"problem.":[145],"They":[146],"include":[147],"volume":[150],"calculation,":[151],"force":[153],"analysis,":[154],"data-driven":[157,199],"deep":[158],"learning":[159],"approach.":[160],"The":[161,183,219],"have":[163],"been":[164],"implemented":[165],"Barrett":[168,190],"then":[171],"evaluated":[172],"simulations":[174],"real":[177,242],"setup":[178],"with":[179],"system.":[182,243],"evaluation":[184],"results":[185],"conclude":[186],"that":[187],"grasps":[192],"multiple":[193],"models":[200],"good":[204],"prediction":[205],"root-mean-square":[220],"errors":[221],"0.74":[223],"for":[224,228,234,238],"balls":[225,235],"0.58":[227],"cubes":[229,239],"simulations,":[231],"1.06":[233],"1.45":[237]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}