{"id":"https://openalex.org/W4414431962","doi":"https://doi.org/10.1109/case58245.2025.11164009","title":"Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting","display_name":"Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting","publication_year":2025,"publication_date":"2025-08-17","ids":{"openalex":"https://openalex.org/W4414431962","doi":"https://doi.org/10.1109/case58245.2025.11164009"},"language":"en","primary_location":{"id":"doi:10.1109/case58245.2025.11164009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case58245.2025.11164009","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":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084641568","display_name":"Emlyn Williams","orcid":null},"institutions":[{"id":"https://openalex.org/I51532219","display_name":"University of Lincoln","ror":"https://ror.org/03yeq9x20","country_code":"GB","type":"education","lineage":["https://openalex.org/I51532219"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Emlyn Williams","raw_affiliation_strings":["University of Lincoln,Lincoln Centre of Autonomous Systems (L-CAS), School of Engineering and Physical Sciences,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Lincoln,Lincoln Centre of Autonomous Systems (L-CAS), School of Engineering and Physical Sciences,United Kingdom","institution_ids":["https://openalex.org/I51532219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035674889","display_name":"Athanasios Polydoros","orcid":"https://orcid.org/0000-0002-4597-0567"},"institutions":[{"id":"https://openalex.org/I51532219","display_name":"University of Lincoln","ror":"https://ror.org/03yeq9x20","country_code":"GB","type":"education","lineage":["https://openalex.org/I51532219"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Athanasios Polydoros","raw_affiliation_strings":["University of Lincoln,Lincoln Centre of Autonomous Systems (L-CAS), School of Engineering and Physical Sciences,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Lincoln,Lincoln Centre of Autonomous Systems (L-CAS), School of Engineering and Physical Sciences,United Kingdom","institution_ids":["https://openalex.org/I51532219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4701,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.89972711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1423","last_page":"1428"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9465000033378601,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9465000033378601,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7918999791145325},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7742999792098999},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5759999752044678},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.48399999737739563},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4546999931335449},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4050999879837036},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.40209999680519104},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.3783000111579895}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7918999791145325},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7742999792098999},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5759999752044678},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5604000091552734},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5471000075340271},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.48399999737739563},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4546999931335449},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3677999973297119},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.3569999933242798},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.34950000047683716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3456999957561493},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3231000006198883},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.30820000171661377},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2944999933242798},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C17500928","wikidata":"https://www.wikidata.org/wiki/Q959968","display_name":"Control system","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2558000087738037},{"id":"https://openalex.org/C116822448","wikidata":"https://www.wikidata.org/wiki/Q1879301","display_name":"Noise control","level":3,"score":0.25279998779296875}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/case58245.2025.11164009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case58245.2025.11164009","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"},{"id":"pmh:oai:figshare.com:article/29236535","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference contribution"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/29236535","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference contribution"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2158782408","https://openalex.org/W2605102758","https://openalex.org/W3004091482","https://openalex.org/W3026254524","https://openalex.org/W3088310808","https://openalex.org/W3207269243","https://openalex.org/W4295549413","https://openalex.org/W4401416214","https://openalex.org/W4413362511","https://openalex.org/W4414079055"],"related_works":[],"abstract_inverted_index":{"This":[0,59],"paper":[1],"presents":[2],"a":[3,17,45,54,124],"novel":[4],"and":[5,39,69,79,102,113,122],"comprehensive":[6],"sim-to-real":[7],"pipeline":[8,106],"for":[9,131],"autonomous":[10,136],"strawberry":[11],"harvesting":[12],"from":[13],"dense":[14],"clusters":[15],"using":[16,92],"Franka":[18],"Panda":[19],"robot.":[20],"Our":[21],"approach":[22],"addresses":[23],"the":[24,34,74,85,93,129],"challenges":[25],"of":[26,36],"robotic":[27],"manipulation":[28],"in":[29,119,123],"unstructured":[30],"agricultural":[31],"environments,":[32],"particularly":[33],"difficulty":[35],"picking":[37],"occluded":[38],"clustered":[40],"fruit.":[41],"We":[42],"introduce":[43],"\"FruitGym\",":[44],"custom":[46],"open-source":[47],"Mujoco":[48],"simulation":[49,121],"environment":[50,60],"designed":[51],"to":[52,72,84,134],"train":[53],"deep":[55],"reinforcement":[56],"learning":[57],"agent.":[58],"leverages":[61],"extensive":[62],"domain":[63],"randomization,":[64],"varying":[65],"lighting,":[66],"object":[67],"placement,":[68],"sensor":[70],"noise":[71],"ensure":[73],"learned":[75],"policy":[76],"is":[77,90],"robust":[78],"can":[80],"be":[81],"transferred":[82],"directly":[83],"real":[86,125],"world.":[87],"The":[88,104],"agent":[89],"trained":[91],"Dormant":[94],"Ratio":[95],"Minimization":[96],"algorithm,":[97],"which":[98],"enhances":[99],"sample":[100],"efficiency":[101],"exploration.":[103],"proposed":[105],"bridges":[107],"low-level":[108],"control":[109],"with":[110],"high-level":[111],"perception":[112],"decision":[114],"making,":[115],"demonstrating":[116],"promising":[117],"performance":[118],"both":[120],"laboratory":[126],"environment,":[127],"laying":[128],"groundwork":[130],"successful":[132],"transfer":[133],"real-world":[135],"fruit":[137],"harvesting.":[138]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}