{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: One-shot imitation learning","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/1e9cc433-5c03-409e-9754-4cd3db1c6f1d","json_url":"https://onlylabs.fyi/signals/1e9cc433-5c03-409e-9754-4cd3db1c6f1d/signal.json","generated_at":"2026-06-08T15:47:16.619+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/1e9cc433-5c03-409e-9754-4cd3db1c6f1d","signal_json":"https://onlylabs.fyi/signals/1e9cc433-5c03-409e-9754-4cd3db1c6f1d/signal.json","source":"https://openai.com/index/one-shot-imitation-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 One-shot imitation learning. This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: One-shot imitation learning | OpenAI March 21, 2017 Publication One-shot imitation learning Read paper Loading… Share Abstract Imitation learning has been commonly.... 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/one-shot-imitation-learning","source_host":"openai.com","occurred_at":"2017-03-21T07:00:00+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 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This talking signal gives public context for research themes, product direction, policy, or launch framing. 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This usually requires either careful feature engineering, or a significant number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning. Specifically, we consider the setting where there is a very large set of tasks, and each task has many instantiations. For example, a task could be to stack all blocks on a table into a single tower, another task could be to place all blocks on a table into two-block towers, etc. In each case, different instances of the task would consist of different sets of blocks with different initial states. At training time, our algorithm is presented with pairs of demonstrations for a subset of all tasks. A neural net is trained that takes as..."},"evidence_pages":[{"url":"https://openai.com/index/one-shot-imitation-learning","final_url":"https://openai.com/index/one-shot-imitation-learning","title":"One-shot imitation learning","http_status":200,"content_type":null,"capture_method":"exa","fetched_at":"2026-06-08T15:47:16.619+00:00","bytes":null,"raw_path":null,"content_hash":null,"excerpt_chars":1200,"truncated":true,"excerpt":"One-shot imitation learning | OpenAI March 21, 2017 Publication One-shot imitation learning Read paper Loading… Share Abstract Imitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a significant number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. 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