{"id":"https://openalex.org/W7131248917","doi":"https://doi.org/10.48550/arxiv.2602.18707","title":"CLASH: Collision Learning via Augmented Sim-to-real Hybridization to Bridge the Reality Gap","display_name":"CLASH: Collision Learning via Augmented Sim-to-real Hybridization to Bridge the Reality Gap","publication_year":2026,"publication_date":"2026-02-21","ids":{"openalex":"https://openalex.org/W7131248917","doi":"https://doi.org/10.48550/arxiv.2602.18707"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.18707","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"type":"preprint","indexed_in":["datacite"],"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/A5056317540","display_name":"Haotian He","orcid":"https://orcid.org/0000-0002-8611-3932"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"He, Haotian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126742800","display_name":"Ning Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Ning","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126735829","display_name":"Siqi Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Siqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029992618","display_name":"Qipeng Liu","orcid":"https://orcid.org/0000-0003-1308-2810"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qipeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126718571","display_name":"Wenzhao Lian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lian, Wenzhao","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5056317540"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.2888999879360199,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.2888999879360199,"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":0.1460999995470047,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.06669999659061432,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/collision","display_name":"Collision","score":0.640500009059906},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.5674999952316284},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.5349000096321106},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5343999862670898},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5289000272750854},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4772999882698059},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.4683000147342682},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.38850000500679016}],"concepts":[{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.640500009059906},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6358000040054321},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.5674999952316284},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.5349000096321106},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5343999862670898},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5289000272750854},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4772999882698059},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.4683000147342682},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.46320000290870667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.446399986743927},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.38850000500679016},{"id":"https://openalex.org/C199668693","wikidata":"https://www.wikidata.org/wiki/Q1550329","display_name":"Collision detection","level":3,"score":0.3531000018119812},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3240000009536743},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C177299277","wikidata":"https://www.wikidata.org/wiki/Q5147505","display_name":"Collision response","level":4,"score":0.266400009393692},{"id":"https://openalex.org/C2781469108","wikidata":"https://www.wikidata.org/wiki/Q7300799","display_name":"Real-time simulation","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C2781018962","wikidata":"https://www.wikidata.org/wiki/Q5164884","display_name":"Container (type theory)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.18707","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},{"id":"doi:10.48550/arxiv.2602.18707","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.18707","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.18707","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,129],"sim-to-real":[1,124],"gap,":[2],"particularly":[3],"in":[4,22,175],"the":[5,143,168,172,185],"inaccurate":[6],"modeling":[7],"of":[8,99,111,146],"contact-rich":[9],"dynamics":[10],"like":[11],"collisions,":[12],"remains":[13],"a":[14,53,58,68,72,78,97],"primary":[15],"obstacle":[16],"to":[17,34,90,121,153,167],"deploying":[18],"robot":[19],"policies":[20,158],"trained":[21],"simulation.":[23],"Conventional":[24],"physics":[25],"engines":[26],"often":[27],"trade":[28],"accuracy":[29,139],"for":[30],"computational":[31],"speed,":[32],"leading":[33],"discrepancies":[35],"that":[36,56,157],"prevent":[37],"direct":[38],"policy":[39],"transfer.":[40],"To":[41],"address":[42],"this,":[43],"we":[44],"introduce":[45],"Collision":[46],"Learning":[47],"via":[48],"Augmented":[49],"Sim-to-real":[50],"Hybridization":[51],"(CLASH),":[52],"data-efficient":[54],"framework":[55],"learns":[57],"parameter-conditioned":[59],"impulsive":[60],"collision":[61],"surrogate":[62],"model":[63,80],"and":[64,115,182],"integrates":[65],"it":[66,105],"as":[67],"plug-in":[69],"module":[70],"within":[71],"standard":[73],"simulator.":[74],"CLASH":[75],"first":[76],"distills":[77],"base":[79],"from":[81],"an":[82],"imperfect":[83],"simulator":[84,132,163],"(MuJoCo)":[85],"using":[86],"large-scale":[87],"simulated":[88],"collisions":[89,101],"capture":[91],"reusable":[92],"physical":[93],"priors.":[94],"Given":[95],"only":[96,134],"handful":[98],"real":[100,169],"(e.g.,":[102],"10":[103],"samples),":[104],"then":[106],"(i)":[107],"performs":[108],"gradient-based":[109],"identification":[110],"key":[112],"contact":[113],"parameters":[114],"(ii)":[116],"applies":[117],"small-step,":[118],"early-stopped":[119],"fine-tuning":[120],"correct":[122],"residual":[123],"mismatches":[125],"while":[126],"avoiding":[127],"overfitting.":[128],"resulting":[130],"hybrid":[131,162],"not":[133],"achieves":[135],"higher":[136],"post-impact":[137],"prediction":[138],"but":[140],"also":[141],"reduces":[142],"wall-clock":[144],"time":[145],"collision-heavy":[147],"CMA-ES":[148],"search":[149],"by":[150],"42-48%":[151],"compared":[152],"MuJoCo.":[154],"We":[155],"demonstrate":[156],"obtained":[159],"with":[160,179,188],"our":[161],"transfer":[164],"more":[165],"robustly":[166],"world,":[170],"doubling":[171],"success":[173],"rate":[174],"sequential":[176],"pushing":[177],"tasks":[178],"reinforcement":[180],"learning":[181],"significantly":[183],"increase":[184],"task":[186],"performance":[187],"model-based":[189],"control.":[190]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-25T00:00:00"}
