{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: Asymmetric actor critic for image-based robot learning","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/f234cfed-3a0c-4bc0-9f16-60d6b777d23b","json_url":"https://onlylabs.fyi/signals/f234cfed-3a0c-4bc0-9f16-60d6b777d23b/signal.json","generated_at":"2026-06-08T15:47:07.697+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/f234cfed-3a0c-4bc0-9f16-60d6b777d23b","signal_json":"https://onlylabs.fyi/signals/f234cfed-3a0c-4bc0-9f16-60d6b777d23b/signal.json","source":"https://openai.com/index/asymmetric-actor-critic-for-image-based-robot-learning","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 Asymmetric actor critic for image-based robot learning. This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Asymmetric actor critic for image-based robot learning | OpenAI October 18, 2017 Asymmetric actor critic for image-based robot learning Loading… Share Abstract Deep.... onlylabs links this event to 1 captured evidence page and 6 related writing signals.","signal_desk":"talking","source_context":{"source_url":"https://openai.com/index/asymmetric-actor-critic-for-image-based-robot-learning","source_host":"openai.com","occurred_at":"2017-10-18T07:00:00+00:00","first_seen_at":"2026-06-05T05:42:57.832854+00:00","date_source":"rss.item_date","context":null},"context_markers":[{"label":"Lab","value":"OpenAI","source":"signal"},{"label":"Signal desk","value":"talking","source":"signal"},{"label":"Source host","value":"openai.com","source":"source"},{"label":"Watch term","value":"RL environments","source":"evidence"},{"label":"Watch term","value":"Eval methodology","source":"evidence"},{"label":"Watch term","value":"Infrastructure","source":"evidence"},{"label":"Watch 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This talking signal gives public context for research themes, product direction, policy, or launch framing. 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However, Robotics poses many challenges for RL, most notably training on a physical system can be expensive and dangerous, which has sparked significant interest in learning control policies using a physics simulator. While several recent works have shown promising results in transferring policies trained in simulation to the real world, they often do not fully utilize the advantage of working with a simulator. In this work, we exploit the full state observability in the simulator to train better policies which take as input only partial observations (RGBD images). We do this by employing an actor-critic training algorithm in which the critic is trained on full states while the actor (or policy) gets rendered images as input. We show experimentally on a range of simulated tasks that using these asymmetric inputs significantly improves performance. Finally, we combine this method with domain randomization and show..."},"evidence_pages":[{"url":"https://openai.com/index/asymmetric-actor-critic-for-image-based-robot-learning","final_url":"https://openai.com/index/asymmetric-actor-critic-for-image-based-robot-learning","title":"Asymmetric actor critic for image-based robot learning","http_status":200,"content_type":null,"capture_method":"exa","fetched_at":"2026-06-08T15:47:07.697+00:00","bytes":null,"raw_path":null,"content_hash":null,"excerpt_chars":1200,"truncated":true,"excerpt":"Asymmetric actor critic for image-based robot learning | OpenAI October 18, 2017 Asymmetric actor critic for image-based robot learning Loading… Share Abstract Deep reinforcement learning (RL) has proven a powerful technique in many sequential decision making domains. However, Robotics poses many challenges for RL, most notably training on a physical system can be expensive and dangerous, which has sparked significant interest in learning control policies using a physics simulator. While several recent works have shown promising results in transferring policies trained in simulation to the real world, they often do not fully utilize the advantage of working with a simulator. In this work, we exploit the full state observability in the simulator to train better policies which take as input only partial observations (RGBD images). We do this by employing an actor-critic training algorithm in which the critic is trained on full states while the actor (or policy) gets rendered images as input. We show experimentally on a range of simulated tasks that using these asymmetric inputs significantly improves performance. 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