{"id":"https://openalex.org/W7165010320","doi":"https://doi.org/10.48550/arxiv.2606.17386","title":"TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations","display_name":"TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7165010320","doi":"https://doi.org/10.48550/arxiv.2606.17386"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.17386","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17386","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.17386","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138809220","display_name":"Zikang Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Zikang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138797586","display_name":"Weixin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Weixin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012448844","display_name":"Zhouchonghao Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zhouchonghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078833755","display_name":"Akshay Rangesh","orcid":"https://orcid.org/0000-0002-4496-6072"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rangesh, Akshay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138812159","display_name":"Saarth Bonde","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bonde, Saarth","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138807468","display_name":"Grantland Hall","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hall, Grantland","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138812016","display_name":"Chen Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138768333","display_name":"Yihan Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138805632","display_name":"Wei Zhan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhan, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.35830000042915344,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.35830000042915344,"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"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.09229999780654907,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.0714000016450882,"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/rendering","display_name":"Rendering (computer graphics)","score":0.5879999995231628},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5716000199317932},{"id":"https://openalex.org/keywords/policy-learning","display_name":"Policy learning","score":0.4952000081539154},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.4805999994277954},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.3935000002384186},{"id":"https://openalex.org/keywords/advanced-driver-assistance-systems","display_name":"Advanced driver assistance systems","score":0.3127000033855438}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.779699981212616},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6103000044822693},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.5879999995231628},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5716000199317932},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.4952000081539154},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.4805999994277954},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4677000045776367},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3935000002384186},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.33629998564720154},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2824000120162964},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2646999955177307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.17386","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17386","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.17386","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17386","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.42655205726623535,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"End-to-end":[0],"autonomous":[1],"driving":[2,27,80],"has":[3],"achieved":[4],"state-of-the-art":[5],"performance":[6],"on":[7,34],"benchmarks":[8],"and":[9,23,31,67,76,119],"real-world":[10],"deployments.":[11],"Its":[12],"standard":[13],"training":[14],"recipe,":[15],"however,":[16],"is":[17,29,36,160],"expensive":[18],"across":[19],"all":[20],"stages:":[21],"collecting":[22],"labeling":[24],"millions":[25,61],"of":[26,42,62,144],"frames":[28,147],"costly,":[30],"closed-loop":[32,168],"RL":[33],"images":[35],"bottlenecked":[37],"by":[38,88,102],"the":[39,59,115,130,153,170],"per-step":[40],"cost":[41],"photorealistic":[43,164],"rendering":[44],"plus":[45],"a":[46,50,68,99,110,120,138,141],"forward":[47],"pass":[48],"through":[49,114],"large":[51],"vision":[52,112],"backbone.":[53],"Self-play":[54],"in":[55,73],"vectorized":[56],"simulators":[57],"changes":[58],"economics:":[60],"rollout":[63],"steps":[64],"per":[65],"second,":[66],"state":[69],"distribution":[70],"naturally":[71],"rich":[72],"collisions,":[74],"near-misses,":[75],"recoveries":[77],"that":[78,157],"no":[79,150],"log":[81],"contains.":[82],"Our":[83],"approach":[84],"exploits":[85],"this":[86],"asymmetry":[87],"decoupling":[89],"learning":[90,94],"to":[91,95],"drive":[92],"from":[93,129],"see.":[96],"We":[97],"pretrain":[98],"single":[100],"policy":[101,173],"self-play,":[103],"then":[104],"align":[105],"its":[106],"latent":[107],"space":[108],"with":[109,149],"pretrained":[111],"backbone,":[113],"action":[116,126],"KL":[117],"divergence":[118],"batch-relational":[121],"low-rank":[122],"structural":[123],"loss.":[124],"The":[125],"target":[127],"comes":[128],"self-play":[131],"policy,":[132],"so":[133],"alignment":[134],"never":[135],"supervises":[136],"against":[137],"logged":[139],"trajectory:":[140],"paired":[142],"dataset":[143],"(image,":[145],"scene-state)":[146],"suffices,":[148],"need":[151],"for":[152],"curated":[154],"expert":[155],"demonstrations":[156],"imitation":[158],"pretraining":[159],"built":[161],"on.":[162],"On":[163],"3D":[165],"Gaussian":[166],"splatting":[167],"scenarios,":[169],"resulting":[171],"end-to-end":[172,178],"matches":[174],"or":[175],"exceeds":[176],"prior":[177],"methods.":[179]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-18T00:00:00"}
