{"id":"https://openalex.org/W4414432254","doi":"https://doi.org/10.1109/case58245.2025.11164159","title":"Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network","display_name":"Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network","publication_year":2025,"publication_date":"2025-08-17","ids":{"openalex":"https://openalex.org/W4414432254","doi":"https://doi.org/10.1109/case58245.2025.11164159"},"language":"en","primary_location":{"id":"doi:10.1109/case58245.2025.11164159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case58245.2025.11164159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)","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/A5081984309","display_name":"Yanlong Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanlong Peng","raw_affiliation_strings":["Shanghai Jiao Tong University,School of Mechanical Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,School of Mechanical Engineering,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085248574","display_name":"Zhigang Wang","orcid":"https://orcid.org/0000-0002-7393-6067"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhigang Wang","raw_affiliation_strings":["Intel Labs China,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intel Labs China,Beijing,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103458634","display_name":"Ziwen He","orcid":"https://orcid.org/0009-0005-1361-1585"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziwen He","raw_affiliation_strings":["Shanghai Jiao Tong University,School of Mechanical Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,School of Mechanical Engineering,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Pengxu Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengxu Chang","raw_affiliation_strings":["Shanghai Jiao Tong University,School of Mechanical Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,School of Mechanical Engineering,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046949895","display_name":"Chuangchuang Zhou","orcid":"https://orcid.org/0000-0002-1232-2184"},"institutions":[{"id":"https://openalex.org/I4210091706","display_name":"Henan Academy of Sciences","ror":"https://ror.org/00hy87220","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210091706"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuangchuang Zhou","raw_affiliation_strings":["Henan Academy of Sciences,Zhengzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Henan Academy of Sciences,Zhengzhou,China","institution_ids":["https://openalex.org/I4210091706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100696875","display_name":"Yan Yu","orcid":"https://orcid.org/0009-0004-3004-3770"},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Yan","raw_affiliation_strings":["Intel CCG FIS,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intel CCG FIS,China","institution_ids":["https://openalex.org/I1343180700"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000326867","display_name":"Ming Chen","orcid":"https://orcid.org/0000-0003-2075-0282"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Chen","raw_affiliation_strings":["Shanghai Jiao Tong University,School of Mechanical Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,School of Mechanical Engineering,China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28290937,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1547","last_page":"1554"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.5378000140190125,"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":0.5378000140190125,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/kinematics","display_name":"Kinematics","score":0.7418000102043152},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6560999751091003},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5796999931335449},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5479000210762024},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5182999968528748},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5116000175476074},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.5110999941825867},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5102999806404114}],"concepts":[{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.7418000102043152},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6840000152587891},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6560999751091003},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6025000214576721},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5796999931335449},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5479000210762024},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5182999968528748},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5116000175476074},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.5110999941825867},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5102999806404114},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4724999964237213},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.44679999351501465},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4341999888420105},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4230000078678131},{"id":"https://openalex.org/C90738871","wikidata":"https://www.wikidata.org/wiki/Q41642869","display_name":"Configuration space","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.35910001397132874},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.3292999863624573},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.31709998846054077},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C150415221","wikidata":"https://www.wikidata.org/wiki/Q40687","display_name":"Robotic arm","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2637999951839447}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/case58245.2025.11164159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case58245.2025.11164159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1660808159","https://openalex.org/W1982184996","https://openalex.org/W2010618743","https://openalex.org/W2149280200","https://openalex.org/W2153628631","https://openalex.org/W2786893837","https://openalex.org/W2792765514","https://openalex.org/W2914044489","https://openalex.org/W2923091270","https://openalex.org/W2963214840","https://openalex.org/W2963299073","https://openalex.org/W3001865277","https://openalex.org/W3043649182","https://openalex.org/W3046016495","https://openalex.org/W3128894241","https://openalex.org/W3152851547","https://openalex.org/W3207190496","https://openalex.org/W4210873969","https://openalex.org/W4306246346","https://openalex.org/W4312803983","https://openalex.org/W4319348618","https://openalex.org/W4394674699","https://openalex.org/W4403677513","https://openalex.org/W4403878347","https://openalex.org/W4405785041","https://openalex.org/W4406613045"],"related_works":[],"abstract_inverted_index":{"In":[0],"robots":[1],"task":[2,156],"and":[3,22,81,96,101,125,145],"motion":[4,28],"planning":[5],"(TAMP),":[6],"it":[7],"is":[8],"crucial":[9],"to":[10,17,31,151],"sample":[11],"within":[12,60],"the":[13,24,32,49,61],"robot\u2019s":[14],"configuration":[15,36,70],"space":[16,107],"meet":[18],"task-level":[19],"global":[20],"constraints":[21,68],"enhance":[23],"efficiency":[25,92],"of":[26,34,51,149],"subsequent":[27],"planning.":[29],"Due":[30],"complexity":[33],"joint":[35],"sampling":[37,59,80,147],"under":[38,66],"multi-level":[39],"constraints,":[40],"traditional":[41,79],"methods":[42],"often":[43],"lack":[44],"efficiency.":[45],"This":[46],"paper":[47],"introduces":[48],"principle":[50],"RobKiNet,":[52],"a":[53,105,116,139,146,154],"kinematics-informed":[54],"neural":[55],"network,":[56],"for":[57],"end-to-end":[58],"Continuous":[62],"Feasible":[63],"Set":[64],"(CFS)":[65],"multiple":[67],"in":[69,104,129,159],"space,":[71],"establishing":[72],"its":[73,109,113],"Optimization":[74],"Expectation":[75],"Model.":[76],"Comparisons":[77],"with":[78,138],"learning-based":[82],"approaches":[83],"reveal":[84],"that":[85],"RobKiNet\u2019s":[86],"kinematic":[87],"knowledge":[88],"infusion":[89],"enhances":[90],"training":[91,140],"by":[93],"ensuring":[94],"stable":[95],"accurate":[97],"gradient":[98],"optimization.":[99],"Visualizations":[100],"quantitative":[102],"analyses":[103],"2-DOF":[106],"validate":[108],"theoretical":[110],"efficiency,":[111],"while":[112],"application":[114],"on":[115],"9-DOF":[117],"autonomous":[118],"mobile":[119],"manipulator":[120],"robot(AMMR)":[121],"demonstrates":[122],"superior":[123],"whole-body":[124],"decoupled":[126],"control,":[127],"excelling":[128],"battery":[130],"disassembly":[131],"tasks.":[132],"RobKiNet":[133],"outperforms":[134],"deep":[135],"reinforcement":[136],"learning":[137],"speed":[141],"74.29":[142],"times":[143],"faster":[144],"accuracy":[148],"up":[150],"99.25%,":[152],"achieving":[153],"97.33%":[155],"completion":[157],"rate":[158],"real-world":[160],"scenarios.":[161]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
