{"id":"https://openalex.org/W3011929509","doi":"https://doi.org/10.1109/cdc40024.2019.9029973","title":"Active Training Trajectory Generation for Inverse Dynamics Model Learning with Deep Neural Networks","display_name":"Active Training Trajectory Generation for Inverse Dynamics Model Learning with Deep Neural Networks","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3011929509","doi":"https://doi.org/10.1109/cdc40024.2019.9029973","mag":"3011929509"},"language":"en","primary_location":{"id":"doi:10.1109/cdc40024.2019.9029973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc40024.2019.9029973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 58th Conference on Decision and Control (CDC)","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/A5048199386","display_name":"Siqi Zhou","orcid":"https://orcid.org/0000-0001-7240-546X"},"institutions":[{"id":"https://openalex.org/I4210162454","display_name":"Dynamic Systems Analysis (Canada)","ror":"https://ror.org/05c2ase32","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210162454"]},{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Siqi Zhou","raw_affiliation_strings":["Dynamic Systems Lab, Institute for Aerospace Studies, University of Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Dynamic Systems Lab, Institute for Aerospace Studies, University of Toronto, Canada","institution_ids":["https://openalex.org/I4210162454","https://openalex.org/I185261750"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052147335","display_name":"Angela P. Schoellig","orcid":"https://orcid.org/0000-0003-4012-4668"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]},{"id":"https://openalex.org/I4210162454","display_name":"Dynamic Systems Analysis (Canada)","ror":"https://ror.org/05c2ase32","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210162454"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Angela P. Schoellig","raw_affiliation_strings":["Dynamic Systems Lab, Institute for Aerospace Studies, University of Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Dynamic Systems Lab, Institute for Aerospace Studies, University of Toronto, Canada","institution_ids":["https://openalex.org/I4210162454","https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5048199386"],"corresponding_institution_ids":["https://openalex.org/I185261750","https://openalex.org/I4210162454"],"apc_list":null,"apc_paid":null,"fwci":0.28,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.68069917,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1784","last_page":"1790"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9998000264167786,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9998000264167786,"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":0.9976999759674072,"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":0.9887999892234802,"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/inverse-dynamics","display_name":"Inverse dynamics","score":0.8542951941490173},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7505856156349182},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7465605735778809},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6209022402763367},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5405731201171875},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5377810597419739},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.4952787458896637},{"id":"https://openalex.org/keywords/trajectory-optimization","display_name":"Trajectory optimization","score":0.4744887351989746},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4477599859237671},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.41823437809944153},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4113311767578125},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10638761520385742},{"id":"https://openalex.org/keywords/kinematics","display_name":"Kinematics","score":0.07868680357933044}],"concepts":[{"id":"https://openalex.org/C187523126","wikidata":"https://www.wikidata.org/wiki/Q17098330","display_name":"Inverse dynamics","level":3,"score":0.8542951941490173},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7505856156349182},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7465605735778809},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6209022402763367},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5405731201171875},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5377810597419739},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.4952787458896637},{"id":"https://openalex.org/C173246807","wikidata":"https://www.wikidata.org/wiki/Q7833062","display_name":"Trajectory optimization","level":3,"score":0.4744887351989746},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4477599859237671},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.41823437809944153},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4113311767578125},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10638761520385742},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.07868680357933044},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cdc40024.2019.9029973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc40024.2019.9029973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 58th Conference on Decision and Control (CDC)","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":28,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1568139284","https://openalex.org/W1969705022","https://openalex.org/W1973240993","https://openalex.org/W2003381716","https://openalex.org/W2055985068","https://openalex.org/W2076118331","https://openalex.org/W2078856056","https://openalex.org/W2095705004","https://openalex.org/W2113564604","https://openalex.org/W2124961364","https://openalex.org/W2151186798","https://openalex.org/W2162991084","https://openalex.org/W2268210938","https://openalex.org/W2903158431","https://openalex.org/W2949071206","https://openalex.org/W2951786554","https://openalex.org/W2962816839","https://openalex.org/W2963238274","https://openalex.org/W2964050632","https://openalex.org/W3103871524","https://openalex.org/W4206723194","https://openalex.org/W4230030242","https://openalex.org/W4231367092","https://openalex.org/W6617145748","https://openalex.org/W6674330103","https://openalex.org/W6730042731","https://openalex.org/W6735374517"],"related_works":["https://openalex.org/W4385832323","https://openalex.org/W4244391535","https://openalex.org/W2356996864","https://openalex.org/W2904060783","https://openalex.org/W2015393961","https://openalex.org/W2687915465","https://openalex.org/W2378339670","https://openalex.org/W2359353485","https://openalex.org/W2139910871","https://openalex.org/W2361427670"],"abstract_inverted_index":{"Inverse":[0],"dynamics":[1,23,41,118],"models":[2],"have":[3,52],"been":[4,53],"used":[5,55],"in":[6,82,153,165],"robot":[7,22,94],"control":[8],"algorithms":[9],"to":[10,16,56,109],"realize":[11],"a":[12,18,33,83,129],"desired":[13],"motion":[14],"or":[15,39,91],"enhance":[17],"robot's":[19],"performance.":[20],"As":[21],"and":[24,149,156],"their":[25],"operating":[26],"environments":[27],"become":[28],"more":[29],"complex,":[30],"there":[31],"is":[32,61],"growing":[34],"trend":[35],"of":[36],"learning":[37,67,137,151],"uncertain":[38],"unknown":[40],"from":[42],"data.":[43],"While":[44],"techniques":[45,148],"such":[46],"as":[47],"deep":[48],"neural":[49],"networks":[50],"(DNNs)":[51],"successfully":[54],"learn":[57],"inverse":[58,117],"dynamics,":[59],"it":[60],"usually":[62],"implicitly":[63],"assumed":[64],"that":[65,106,127,169],"the":[66,158,170],"modules":[68],"are":[69],"trained":[70],"on":[71],"sufficiently":[72],"rich":[73],"datasets.":[74],"In":[75,96,120],"practical":[76],"implementations,":[77],"this":[78,97],"assumption":[79],"typically":[80],"results":[81],"trial-and-error":[84],"training":[85,115,161,173,179],"process,":[86],"which":[87],"can":[88],"be":[89],"inefficient":[90],"unsafe":[92],"for":[93,114,138],"applications.":[95],"paper,":[98],"we":[99,122],"present":[100],"an":[101,124],"active":[102,136,150,160,172],"trajectory":[103,131,162,174],"generation":[104,163,175],"framework":[105],"allows":[107],"us":[108],"systematically":[110],"design":[111],"informative":[112],"trajectories":[113],"DNN":[116,135,145],"modules.":[119],"particular,":[121],"introduce":[123],"episode-based":[125],"algorithm":[126],"integrates":[128],"spline":[130],"optimization":[132],"approach":[133,164],"with":[134],"efficient":[139],"data":[140],"collection.":[141],"We":[142,167],"consider":[143],"different":[144],"uncertainty":[146],"estimation":[147],"heuristics":[152],"our":[154],"work":[155],"illustrate":[157],"proposed":[159,171],"simulation.":[166],"show":[168],"outperforms":[176],"adhoc,":[177],"intuitive":[178],"approaches.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}