{"id":"https://openalex.org/W4401415528","doi":"https://doi.org/10.1109/icra57147.2024.10611601","title":"Towards Feasible Dynamic Grasping: Leveraging Gaussian Process Distance Field, SE(3) Equivariance, and Riemannian Mixture Models","display_name":"Towards Feasible Dynamic Grasping: Leveraging Gaussian Process Distance Field, SE(3) Equivariance, and Riemannian Mixture Models","publication_year":2024,"publication_date":"2024-05-13","ids":{"openalex":"https://openalex.org/W4401415528","doi":"https://doi.org/10.1109/icra57147.2024.10611601"},"language":"en","primary_location":{"id":"doi:10.1109/icra57147.2024.10611601","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra57147.2024.10611601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 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/A5108440699","display_name":"Ho Jin Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ho Jin Choi","raw_affiliation_strings":["University of Pennsylvania,School of Engineering and Applied Science,Department of Mechanical Engineering and Applied Mechanics,Pennsylvania,PA,USA,19104"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania,School of Engineering and Applied Science,Department of Mechanical Engineering and Applied Mechanics,Pennsylvania,PA,USA,19104","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074348852","display_name":"Nadia Figueroa","orcid":"https://orcid.org/0000-0002-6873-4671"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nadia Figueroa","raw_affiliation_strings":["University of Pennsylvania,School of Engineering and Applied Science,Department of Mechanical Engineering and Applied Mechanics,Pennsylvania,PA,USA,19104"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania,School of Engineering and Applied Science,Department of Mechanical Engineering and Applied Mechanics,Pennsylvania,PA,USA,19104","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2219,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.82601438,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"6455","last_page":"6461"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9876000285148621,"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"}},"topics":[{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9876000285148621,"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"}},{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9757000207901001,"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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.9549999833106995,"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/gaussian-process","display_name":"Gaussian process","score":0.7046383023262024},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.671226441860199},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.576046884059906},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5290716290473938},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.37677472829818726},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3622255325317383},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.32185348868370056},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.18447351455688477},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1254865527153015}],"concepts":[{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.7046383023262024},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.671226441860199},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.576046884059906},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5290716290473938},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.37677472829818726},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3622255325317383},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32185348868370056},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.18447351455688477},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1254865527153015},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra57147.2024.10611601","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra57147.2024.10611601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Robotics and Automation (ICRA)","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/W1806263934","https://openalex.org/W2018020788","https://openalex.org/W2030929340","https://openalex.org/W2140173255","https://openalex.org/W2524140598","https://openalex.org/W2915178317","https://openalex.org/W2963627347","https://openalex.org/W2986303149","https://openalex.org/W3015812680","https://openalex.org/W3040653306","https://openalex.org/W3090292349","https://openalex.org/W3158545781","https://openalex.org/W3206683563","https://openalex.org/W3207187156","https://openalex.org/W3212320963","https://openalex.org/W4205483505","https://openalex.org/W4285102336","https://openalex.org/W4285178161","https://openalex.org/W4285273130","https://openalex.org/W4382322597","https://openalex.org/W4382366145","https://openalex.org/W4383097758","https://openalex.org/W4383108224","https://openalex.org/W4383108955","https://openalex.org/W4383109488","https://openalex.org/W4385403781","https://openalex.org/W4386071618","https://openalex.org/W4386075727","https://openalex.org/W4390204274","https://openalex.org/W6854362512"],"related_works":["https://openalex.org/W2938786841","https://openalex.org/W2982120024","https://openalex.org/W2140288152","https://openalex.org/W2963637926","https://openalex.org/W2799769525","https://openalex.org/W2048140462","https://openalex.org/W1966851638","https://openalex.org/W2101272603","https://openalex.org/W1964286703","https://openalex.org/W2169866437"],"abstract_inverted_index":{"This":[0,125],"paper":[1],"introduces":[2],"a":[3,98,166],"novel":[4,110],"approach":[5,39,146],"to":[6,30,33,78,94],"improve":[7],"robotic":[8,171],"grasping":[9,102,137,172],"in":[10,85,147,156,174],"dynamic":[11,86,157],"environments":[12],"by":[13,109],"integrating":[14,160],"Gaussian":[15,89,119,123],"Process":[16],"Distance":[17],"Fields":[18],"(GPDF),":[19],"SE(3)":[20,68],"equivariant":[21,77],"networks,":[22],"and":[23,49,100,122,152],"Riemannian":[24,88],"Mixture":[25,90,120],"Models.":[26],"The":[27],"aim":[28],"is":[29,62],"enable":[31],"robots":[32],"grasp":[34,47,51,66,74,105,132,150],"moving":[35],"objects":[36],"effectively.":[37],"Our":[38],"comprises":[40],"three":[41],"main":[42],"components:":[43],"object":[44],"shape":[45,58],"reconstruction,":[46],"sampling,":[48],"implicit":[50],"pose":[52,81,133],"selection.":[53],"GPDF":[54],"accurately":[55],"models":[56],"the":[57,72,79,128,142],"of":[59,130,144],"objects,":[60],"which":[61],"essential":[63],"for":[64,169],"precise":[65],"planning.":[67],"equivariance":[69],"ensures":[70],"that":[71],"sampled":[73],"poses":[75,106,151],"are":[76,92,107],"object\u2019s":[80],"changes,":[82],"enhancing":[83,170],"robustness":[84],"scenarios.":[87,176],"Models":[91,121],"employed":[93],"assess":[95],"reachability,":[96],"providing":[97],"feasible":[99,149],"adaptable":[101],"strategies.":[103],"Feasible":[104],"targeted":[108],"task":[111],"or":[112],"joint":[113],"space":[114],"reactive":[115],"controllers":[116],"formulated":[117],"using":[118],"Processes.":[124],"method":[126],"resolves":[127],"challenge":[129],"discrete":[131],"selection,":[134],"enabling":[135],"smoother":[136],"execution.":[138],"Experimental":[139],"validation":[140],"confirms":[141],"effectiveness":[143],"our":[145],"generating":[148],"achieving":[153],"successful":[154],"grasps":[155],"environments.":[158],"By":[159],"these":[161],"advanced":[162],"techniques,":[163],"we":[164],"present":[165],"promising":[167],"solution":[168],"capabilities":[173],"real-world":[175]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
