{"id":"https://openalex.org/W2006833873","doi":"https://doi.org/10.1109/iros.2014.6943029","title":"Evaluating the efficacy of grasp metrics for utilization in a Gaussian Process-based grasp predictor","display_name":"Evaluating the efficacy of grasp metrics for utilization in a Gaussian Process-based grasp predictor","publication_year":2014,"publication_date":"2014-09-01","ids":{"openalex":"https://openalex.org/W2006833873","doi":"https://doi.org/10.1109/iros.2014.6943029","mag":"2006833873"},"language":"en","primary_location":{"id":"doi:10.1109/iros.2014.6943029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2014.6943029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE/RSJ International Conference on Intelligent Robots and Systems","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/A5014772085","display_name":"Alex K. Goins","orcid":"https://orcid.org/0000-0003-4429-6774"},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alex K. Goins","raw_affiliation_strings":["School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, USA","Oregon State University, Corvallis, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, USA","institution_ids":["https://openalex.org/I131249849"]},{"raw_affiliation_string":"Oregon State University, Corvallis, United States","institution_ids":["https://openalex.org/I131249849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086791917","display_name":"Ryan Carpenter","orcid":null},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ryan Carpenter","raw_affiliation_strings":["School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, USA","Oregon State University, Corvallis, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, USA","institution_ids":["https://openalex.org/I131249849"]},{"raw_affiliation_string":"Oregon State University, Corvallis, United States","institution_ids":["https://openalex.org/I131249849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066210234","display_name":"Weng\u2010Keen Wong","orcid":"https://orcid.org/0000-0002-6673-343X"},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weng-Keen Wong","raw_affiliation_strings":["School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA","Oregon State University, Corvallis, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA","institution_ids":["https://openalex.org/I131249849"]},{"raw_affiliation_string":"Oregon State University, Corvallis, United States","institution_ids":["https://openalex.org/I131249849"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066817859","display_name":"Ravi Balasubramanian","orcid":"https://orcid.org/0000-0001-7472-6603"},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ravi Balasubramanian","raw_affiliation_strings":["School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, USA","Oregon State University, Corvallis, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, USA","institution_ids":["https://openalex.org/I131249849"]},{"raw_affiliation_string":"Oregon State University, Corvallis, United States","institution_ids":["https://openalex.org/I131249849"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.2831,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.92031694,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"3","issue":null,"first_page":"3353","last_page":"3360"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9995999932289124,"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":0.9995999932289124,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9269999861717224,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9818540811538696},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7522346377372742},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6528022885322571},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6495110988616943},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6103110313415527},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5610092878341675},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.558119535446167},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.49084264039993286},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4623846113681793},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.4248335063457489},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4247328042984009},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3039180040359497}],"concepts":[{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.9818540811538696},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7522346377372742},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6528022885322571},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6495110988616943},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6103110313415527},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5610092878341675},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.558119535446167},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.49084264039993286},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4623846113681793},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4248335063457489},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4247328042984009},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3039180040359497},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iros.2014.6943029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2014.6943029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE/RSJ International Conference on Intelligent Robots and Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.718.5468","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.718.5468","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://web.engr.oregonstate.edu/%7Ebalasubr/pub/Balasubramanian-Grasping-IROS-2014.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W46565623","https://openalex.org/W562639461","https://openalex.org/W1510186039","https://openalex.org/W1794703952","https://openalex.org/W1978580730","https://openalex.org/W1980602022","https://openalex.org/W1999375810","https://openalex.org/W2026499961","https://openalex.org/W2038245875","https://openalex.org/W2053940671","https://openalex.org/W2058212397","https://openalex.org/W2060467095","https://openalex.org/W2088043683","https://openalex.org/W2106628124","https://openalex.org/W2109163007","https://openalex.org/W2112787507","https://openalex.org/W2116817751","https://openalex.org/W2120200810","https://openalex.org/W2134634036","https://openalex.org/W2134858017","https://openalex.org/W2135085314","https://openalex.org/W2136354320","https://openalex.org/W2138983671","https://openalex.org/W2150018021","https://openalex.org/W2156186728","https://openalex.org/W2157825442","https://openalex.org/W2158533927","https://openalex.org/W2167788274","https://openalex.org/W2169241897","https://openalex.org/W2280111819","https://openalex.org/W2288286171","https://openalex.org/W2939294015","https://openalex.org/W3112422759","https://openalex.org/W4211049957","https://openalex.org/W6659930351","https://openalex.org/W6684916627","https://openalex.org/W6787597433"],"related_works":["https://openalex.org/W2163296013","https://openalex.org/W165915117","https://openalex.org/W2326995835","https://openalex.org/W2743859443","https://openalex.org/W2059402478","https://openalex.org/W2123347777","https://openalex.org/W4387804363","https://openalex.org/W2477150073","https://openalex.org/W2019547100","https://openalex.org/W4387947522"],"abstract_inverted_index":{"With":[0],"the":[1,5,37,62,72,93,114,118,134,157,166],"goal":[2],"of":[3,7,39,61,64,113,123,177,186],"advancing":[4],"state":[6],"automatic":[8],"robotic":[9,27,41,131],"grasping,":[10],"we":[11,54],"present":[12],"a":[13,25,40,47,57,99,129,161,172],"novel":[14],"approach":[15],"that":[16,35,67,101],"combines":[17],"machine":[18],"learning":[19],"techniques":[20,142],"and":[21,79,88],"rigorous":[22],"validation":[23],"on":[24,92,128,184],"physical":[26,130],"platform":[28],"in":[29,71,117,165],"order":[30],"to":[31,75,97],"develop":[32],"an":[33,145],"algorithm":[34],"predicts":[36,102],"quality":[38,125,148],"grasp":[42,49,65,95,103,115,124,140,147,154,188],"before":[43],"execution.":[44],"After":[45],"collecting":[46],"large":[48],"sample":[50],"set":[51],"(522":[52],"grasps),":[53],"first":[55],"conduct":[56],"thorough":[58],"statistical":[59],"analysis":[60],"ability":[63],"metrics":[66,96,116],"are":[68,108,120],"commonly":[69],"used":[70],"robotics":[73],"literature":[74,119],"discriminate":[76],"between":[77],"good":[78],"bad":[80],"grasps.":[81],"We":[82],"then":[83],"apply":[84],"Principal":[85],"Component":[86],"Analysis":[87],"Gaussian":[89,135],"Process":[90],"algorithms":[91],"discriminative":[94],"build":[98],"classifier":[100,137,159],"quality.":[104],"The":[105],"key":[106],"findings":[107],"as":[109],"follows:":[110],"(i)":[111],"several":[112],"weak":[121],"predictors":[122],"when":[126,179],"implemented":[127],"platform;":[132],"(ii)":[133],"Process-based":[136],"significantly":[138],"improves":[139],"prediction":[141,149],"by":[143],"providing":[144],"absolute":[146],"score":[150],"from":[151],"combining":[152],"multiple":[153],"metrics.":[155,189],"Specifically,":[156],"GP":[158],"showed":[160],"66%":[162],"percent":[163],"improvement":[164],"True":[167],"Positive":[168,175],"classification":[169,182],"rate":[170,176],"at":[171],"low":[173],"False":[174],"5%":[178],"compared":[180],"with":[181],"based":[183],"thresholding":[185],"individual":[187]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":1}],"updated_date":"2026-06-12T08:23:45.883708","created_date":"2025-10-10T00:00:00"}
