{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/domain-randomization-for-transferring-deep","title":"Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World","arxiv_id":"1703.06907","date":"2017-03-20","proceeding":null,"authors":["Josh Tobin","Rachel Fong","Alex Ray","Jonas Schneider","Wojciech Zaremba","Pieter Abbeel"],"abstract":"Bridging the 'reality gap' that separates simulated robotics from experiments\non hardware could accelerate robotic research through improved data\navailability. This paper explores domain randomization, a simple technique for\ntraining models on simulated images that transfer to real images by randomizing\nrendering in the simulator. With enough variability in the simulator, the real\nworld may appear to the model as just another variation. We focus on the task\nof object localization, which is a stepping stone to general robotic\nmanipulation skills. We find that it is possible to train a real-world object\ndetector that is accurate to $1.5$cm and robust to distractors and partial\nocclusions using only data from a simulator with non-realistic random textures.\nTo demonstrate the capabilities of our detectors, we show they can be used to\nperform grasping in a cluttered environment. To our knowledge, this is the\nfirst successful transfer of a deep neural network trained only on simulated\nRGB images (without pre-training on real images) to the real world for the\npurpose of robotic control.","url_abs":"http://arxiv.org/abs/1703.06907v1","url_pdf":"http://arxiv.org/pdf/1703.06907v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"domain-randomization-for-transferring-deep","repo_url":"https://github.com/facebookresearch/dcd","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"domain-randomization-for-transferring-deep","repo_url":"https://github.com/facebookresearch/minimax","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null},{"paper_slug":"domain-randomization-for-transferring-deep","repo_url":"https://github.com/mrahtz/learning-from-human-preferences","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"domain-randomization-for-transferring-deep","repo_url":"https://github.com/mveres01/grasping","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"domain-randomization-for-transferring-deep","repo_url":"https://github.com/neka-nat/gazebo_domain_randomization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"domain-randomization-for-transferring-deep","repo_url":"https://github.com/vict0rsch/PaperMemory","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"domain-randomization-for-transferring-deep","repo_url":"https://github.com/xinjinghao/sparrow-v1","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object-localization","task_name":"Object Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.06907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.06907"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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