{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: Sim-to-real transfer of robotic control with dynamics randomization","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/fd6eea0d-427d-4197-8438-b09b122a2d20","json_url":"https://onlylabs.fyi/signals/fd6eea0d-427d-4197-8438-b09b122a2d20/signal.json","generated_at":"2026-06-08T15:47:07.274+00:00","org":{"slug":"openai","name":"OpenAI","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/openai","dossier_json_url":"https://onlylabs.fyi/labs/openai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/fd6eea0d-427d-4197-8438-b09b122a2d20","signal_json":"https://onlylabs.fyi/signals/fd6eea0d-427d-4197-8438-b09b122a2d20/signal.json","source":"https://openai.com/index/sim-to-real-transfer-of-robotic-control-with-dynamics-randomization","lab_dossier":"https://onlylabs.fyi/labs/openai","lab_dossier_json":"https://onlylabs.fyi/labs/openai/dossier.json","analysis":"https://onlylabs.fyi/analysis/openai","analysis_json":"https://onlylabs.fyi/analysis/openai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/openai/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml","data_business":null},"answer_pack":{"answer":"OpenAI published Sim-to-real transfer of robotic control with dynamics randomization. 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This talking signal gives public context for research themes, product direction, policy, or launch framing. 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But the behaviours developed by agents in simulation are often specific to the characteristics of the simulator. Due to modeling error, strategies that are successful in simulation may not transfer to their real world counterparts. In this paper, we demonstrate a simple method to bridge this \"reality gap\". By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained. This adaptivity enables the policies to generalize to the dynamics of the real world without any training on the physical system. Our approach is demonstrated on an object pushing task using a robotic arm. Despite being trained exclusively in simulation, our policies are able to maintain..."},"evidence_pages":[{"url":"https://openai.com/index/sim-to-real-transfer-of-robotic-control-with-dynamics-randomization","final_url":"https://openai.com/index/sim-to-real-transfer-of-robotic-control-with-dynamics-randomization","title":"Sim-to-real transfer of robotic control with dynamics randomization","http_status":200,"content_type":null,"capture_method":"exa","fetched_at":"2026-06-08T15:47:07.274+00:00","bytes":null,"raw_path":null,"content_hash":null,"excerpt_chars":1200,"truncated":true,"excerpt":"Sim-to-real transfer of robotic control with dynamics randomization | OpenAI October 18, 2017 Sim-to-real transfer of robotic control with dynamics randomization Loading… Share Abstract Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the training process. But the behaviours developed by agents in simulation are often specific to the characteristics of the simulator. Due to modeling error, strategies that are successful in simulation may not transfer to their real world counterparts. In this paper, we demonstrate a simple method to bridge this \"reality gap\". By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained. This adaptivity enables the policies to generalize to the dynamics of the real world without any training on the physical system. Our approach is demonstrated on an object pushing task using a robotic arm. 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