{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T03:50:08Z","timestamp":1775620208359,"version":"3.50.1"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000053","name":"National Eye Institute","doi-asserted-by":"publisher","award":["R61EY037527"],"award-info":[{"award-number":["R61EY037527"]}],"id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2318101"],"award-info":[{"award-number":["2318101"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006234","name":"Sandia National Laboratories","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006234","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1109\/lra.2026.3671536","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T20:00:15Z","timestamp":1773086415000},"page":"5749-5756","source":"Crossref","is-referenced-by-count":0,"title":["Value Explicit Pretraining for Learning Transferable Representations"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0731-3918","authenticated-orcid":false,"given":"Kiran","family":"Lekkala","sequence":"first","affiliation":[{"name":"Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9124-5079","authenticated-orcid":false,"given":"Henghui","family":"Bao","sequence":"additional","affiliation":[{"name":"Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, CA, USA"}]},{"given":"Sumedh A.","family":"Sontakke","sequence":"additional","affiliation":[{"name":"Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9516-3130","authenticated-orcid":false,"given":"Erdem","family":"Byk","sequence":"additional","affiliation":[{"name":"Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, CA, USA"}]},{"given":"Laurent","family":"Itti","sequence":"additional","affiliation":[{"name":"Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, CA, USA"}]}],"member":"263","reference":[{"key":"ref1","first-page":"9465","article-title":"RRL: Resnet as representation for reinforcement learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Shah","year":"2021"},{"key":"ref2","first-page":"13022","article-title":"Pre-trained image encoder for generalizable visual reinforcement learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Yuan"},{"key":"ref3","first-page":"17359","article-title":"The unsurprising effectiveness of pre-trained vision models for control","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Parisi","year":"2022"},{"key":"ref4","article-title":"AutoFocus-IL: VLM-based saliency maps for data-efficient visual imitation learning without extra human annotations","author":"Gong","year":"2025"},{"key":"ref5","first-page":"892","article-title":"R3M: A universal visual representation for robot manipulation","volume-title":"Proc. Conf. Robot Learn.","author":"Nair","year":"2023"},{"key":"ref6","article-title":"VIP: Towards universal visual reward and representation via value-implicit pre-training","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Ma","year":"2023"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2023.XIX.032"},{"key":"ref8","first-page":"416","article-title":"Real-world robot learning with masked visual pre-training","volume-title":"Proc. Conf. Robot Learn.","author":"Radosavovic","year":"2023"},{"key":"ref9","article-title":"Masked visual pre-training for motor control","author":"Xiao","year":"2022"},{"key":"ref10","first-page":"1332","article-title":"Masked world models for visual control","volume-title":"Proc. Conf. Robot Learn.","author":"Seo","year":"2023"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/IROS58592.2024.10801388"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2580"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.07.006"},{"key":"ref14","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma","year":"2014"},{"key":"ref15","article-title":"Beta-VAE: Learning basic visual concepts with a constrained variational framework","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Higgins","year":"2017"},{"key":"ref16","first-page":"2451","article-title":"Recurrent world models facilitate policy evolution","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Ha"},{"key":"ref17","article-title":"Unsupervised state representation learning in atari","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Anand"},{"key":"ref18","article-title":"Pretraining in deep reinforcement learning: A survey","author":"Xie","year":"2022"},{"key":"ref19","first-page":"5062","article-title":"Self-supervised exploration via disagreement","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pathak","year":"2019"},{"key":"ref20","first-page":"12686","article-title":"Pretraining representations for data-efficient reinforcement learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Schwarzer"},{"key":"ref21","article-title":"Progressive neural networks","author":"Rusu","year":"2016"},{"key":"ref22","first-page":"2063","article-title":"Transfer learning for related reinforcement learning tasks via image-to-image translation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gamrian","year":"2019"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8462891"},{"key":"ref24","article-title":"Rl$^{2}$: Fast reinforcement learning via slow reinforcement learning","author":"Duan","year":"2016"},{"key":"ref25","first-page":"1126","article-title":"Model-agnostic meta -learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn","year":"2017"},{"key":"ref26","first-page":"20532","article-title":"Information-theoretic task selection for meta-reinforcement learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Gutierrez","year":"2020"},{"key":"ref27","article-title":"Robotic offline RL from internet videos via value-function pre-training","volume-title":"Proc. NeurIPS 2023 Found. Models Decis. Mak. Workshop","author":"Bhateja","year":"2023"},{"key":"ref28","first-page":"2025","article-title":"Rewind: Language-guided rewards teach robot policies without new demonstrations","volume-title":"Proc. Conf. Robot Learn.","author":"Zhang"},{"key":"ref29","article-title":"Accelerating exploration and representation learning with offline pre-training","author":"Mazoure","year":"2023"},{"key":"ref30","first-page":"6574","article-title":"DMC-VB: A benchmark for representation learning for control with visual distractors","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"37","author":"Ortiz","year":"2024"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref32","article-title":"Representation learning with contrastive predictive coding","author":"Oord","year":"2018"},{"key":"ref33","article-title":"D4RL: Datasets for deep data-driven reinforcement learning","author":"Fu","year":"2020"},{"key":"ref34","article-title":"The streetlearn environment and dataset","author":"Mirowski","year":"2019"},{"key":"ref35","article-title":"Openai gym","author":"Brockman","year":"2016"},{"key":"ref36","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017"},{"key":"ref37","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Radford","year":"2021"},{"key":"ref38","first-page":"655","article-title":"Where are we in the search for an artificial visual cortex for embodied intelligence?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Majumdar","year":"2023"},{"key":"ref39","first-page":"1861","article-title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Haarnoja","year":"2018"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/7083369\/11435997\/11425763-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7083369\/11435997\/11425763.pdf?arnumber=11425763","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T02:51:07Z","timestamp":1775616667000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11425763\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":39,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/lra.2026.3671536","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]}}}