{"id":"https://openalex.org/W4383108188","doi":"https://doi.org/10.1109/icra48891.2023.10161267","title":"Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors","display_name":"Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors","publication_year":2023,"publication_date":"2023-05-29","ids":{"openalex":"https://openalex.org/W4383108188","doi":"https://doi.org/10.1109/icra48891.2023.10161267"},"language":"en","primary_location":{"id":"doi:10.1109/icra48891.2023.10161267","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48891.2023.10161267","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/A5034039171","display_name":"Shikha Surana","orcid":"https://orcid.org/0009-0007-1701-6876"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Shikha Surana","raw_affiliation_strings":["Imperial College London,United Kingdom","Imperial College London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Imperial College London,United Kingdom","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"Imperial College London, United Kingdom","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011437628","display_name":"Bryan Lim","orcid":"https://orcid.org/0000-0002-2324-1400"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Bryan Lim","raw_affiliation_strings":["Imperial College London,United Kingdom","Imperial College London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Imperial College London,United Kingdom","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"Imperial College London, United Kingdom","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011747084","display_name":"Antoine Cully","orcid":"https://orcid.org/0000-0002-3190-7073"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Antoine Cully","raw_affiliation_strings":["Imperial College London,United Kingdom","Imperial College London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Imperial College London,United Kingdom","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"Imperial College London, United Kingdom","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034039171"],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":0.5644,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65387579,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"12134","last_page":"12141"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9983999729156494,"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":0.9983999729156494,"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/T10879","display_name":"Robotic Locomotion and Control","score":0.9959999918937683,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9829000234603882,"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/prior-probability","display_name":"Prior probability","score":0.8131303191184998},{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.8068398237228394},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7447701692581177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7278876304626465},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.6322885751724243},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5567768812179565},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5443207621574402},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5237988233566284},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4984312057495117},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.49763062596321106},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4308048188686371},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.41804179549217224},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1275007426738739},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.10614711046218872}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.8131303191184998},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.8068398237228394},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7447701692581177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7278876304626465},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.6322885751724243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5567768812179565},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5443207621574402},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5237988233566284},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4984312057495117},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.49763062596321106},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4308048188686371},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.41804179549217224},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1275007426738739},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.10614711046218872},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra48891.2023.10161267","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48891.2023.10161267","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G866405208","display_name":"REcoVER: Learning algorithms for REsilient and VErsatile Robots","funder_award_id":"EP/V006673/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G8782551874","display_name":null,"funder_award_id":"EP/V006673/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W758372786","https://openalex.org/W1738827650","https://openalex.org/W1965675101","https://openalex.org/W2053153219","https://openalex.org/W2065238107","https://openalex.org/W2120033649","https://openalex.org/W2138537392","https://openalex.org/W2462548332","https://openalex.org/W2473761223","https://openalex.org/W2606327391","https://openalex.org/W2788030459","https://openalex.org/W2797210984","https://openalex.org/W2909331752","https://openalex.org/W2909553221","https://openalex.org/W2911087563","https://openalex.org/W2954700257","https://openalex.org/W2962687375","https://openalex.org/W2962736495","https://openalex.org/W2963776872","https://openalex.org/W2968213087","https://openalex.org/W2972810470","https://openalex.org/W2981030070","https://openalex.org/W3001500946","https://openalex.org/W3004647014","https://openalex.org/W3011912079","https://openalex.org/W3018036994","https://openalex.org/W3090207068","https://openalex.org/W3093922502","https://openalex.org/W3103202942","https://openalex.org/W3104767570","https://openalex.org/W3104876774","https://openalex.org/W3112009288","https://openalex.org/W3129322645","https://openalex.org/W3175254947","https://openalex.org/W3175662569","https://openalex.org/W3199753215","https://openalex.org/W3200285024","https://openalex.org/W3206762371","https://openalex.org/W3207033168","https://openalex.org/W3213974477","https://openalex.org/W4205430897","https://openalex.org/W4210911782","https://openalex.org/W4223426965","https://openalex.org/W4250058668","https://openalex.org/W4283787029","https://openalex.org/W4295113716","https://openalex.org/W4300716756","https://openalex.org/W6622219343","https://openalex.org/W6748929038","https://openalex.org/W6755667589","https://openalex.org/W6762581443","https://openalex.org/W6767349745","https://openalex.org/W6775338258","https://openalex.org/W6800004206","https://openalex.org/W6801964084"],"related_works":["https://openalex.org/W2377402383","https://openalex.org/W2380835401","https://openalex.org/W2381912691","https://openalex.org/W2350381577","https://openalex.org/W2353618196","https://openalex.org/W2348074676","https://openalex.org/W2385033175","https://openalex.org/W4200195314","https://openalex.org/W4389574494","https://openalex.org/W2156329176"],"abstract_inverted_index":{"Data-driven":[0],"learning":[1,10,40,139],"based":[2],"methods":[3],"have":[4],"recently":[5],"been":[6],"particularly":[7],"successful":[8],"at":[9,38],"robust":[11],"locomotion":[12,27,42,92],"controllers":[13],"for":[14],"a":[15,46,57,83,87,99,115,121,140,155,162],"variety":[16],"of":[17,32,70,90,124,143,165],"unstructured":[18],"terrains.":[19],"Prior":[20],"work":[21],"has":[22],"shown":[23],"that":[24,85,112,138],"incorporating":[25],"good":[26],"priors":[28,93,146],"in":[29],"the":[30,68,71,103],"form":[31],"trajectory":[33],"generators":[34],"(TGs)":[35],"is":[36,147],"effective":[37],"efficiently":[39],"complex":[41,55],"skills.":[43],"However,":[44],"defining":[45],"good,":[47],"single":[48,100],"TG":[49,106,145],"as":[50,60,127],"tasks/environments":[51],"become":[52],"increasingly":[53],"more":[54,151],"remains":[56],"challenging":[58],"problem":[59],"it":[61],"requires":[62],"extensive":[63],"tuning":[64],"and":[65,132],"risks":[66],"reducing":[67],"effectiveness":[69],"prior.":[72],"In":[73],"this":[74],"paper,":[75],"we":[76],"present":[77],"Evolved":[78],"Environmental":[79],"Trajectory":[80],"Generators":[81],"(EETG),":[82],"method":[84],"learns":[86],"diverse":[88,141],"set":[89,142],"specialised":[91],"using":[94,154],"Quality-Diversity":[95],"algorithms":[96],"while":[97],"maintaining":[98],"policy":[101],"within":[102],"Policies":[104],"Modulating":[105],"(PMTG)":[107],"architecture.":[108],"The":[109],"results":[110],"demonstrate":[111],"EETG":[113],"enables":[114],"quadruped":[116],"robot":[117],"to":[118],"successfully":[119],"traverse":[120],"wide":[122,163],"range":[123,164],"environments,":[125],"such":[126],"slopes,":[128],"stairs,":[129],"rough":[130],"terrain,":[131],"balance":[133],"beams.":[134],"Our":[135],"experiments":[136],"show":[137],"specialized":[144],"significantly":[148],"(5":[149],"times)":[150],"efficient":[152],"than":[153],"single,":[156],"fixed":[157],"prior":[158],"when":[159],"dealing":[160],"with":[161],"environments.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}