{"id":6133,"date":"2023-10-05T12:53:51","date_gmt":"2023-10-05T12:53:51","guid":{"rendered":"https:\/\/dailyai.com\/?p=6133"},"modified":"2023-10-05T12:53:51","modified_gmt":"2023-10-05T12:53:51","slug":"open-x-embodiment-dataset-rt-x-model-a-leap-for-ai-robots","status":"publish","type":"post","link":"https:\/\/dailyai.com\/it\/2023\/10\/open-x-embodiment-dataset-rt-x-model-a-leap-for-ai-robots\/","title":{"rendered":"Il dataset aperto X-Embodiment, il modello RT-X, un salto di qualit\u00e0 per i robot AI"},"content":{"rendered":"<p><strong>DeepMind di Google ha collaborato con 33 diversi laboratori accademici per creare un set di dati per l'addestramento dell'IA basato su 22 diversi tipi di robot.<\/strong><\/p>\n<p>I robot sono molto bravi a fare una cosa specifica. Se si vuole che facciano qualcosa di leggermente diverso, il robot deve essere addestrato da zero. L'obiettivo finale della robotica \u00e8 avere un robot che sia bravo in una gamma generale di azioni e che sia in grado di apprendere da solo nuove abilit\u00e0.<\/p>\n<p>Per addestrare un modello di intelligenza artificiale \u00e8 necessario un ampio set di dati relativi allo scopo del modello. Modelli linguistici come <a href=\"https:\/\/dailyai.com\/it\/2023\/09\/openai-reveals-new-voice-and-image-features-for-chatgpt\/\">GPT-4<\/a> sono addestrati su grandi quantit\u00e0 di dati scritti. Generatori di immagini come <a href=\"https:\/\/dailyai.com\/it\/2023\/10\/dall-e-3-ai-image-generator-available-free-on-bing-chat\/\">DALL-E 3<\/a> sono addestrati su grandi quantit\u00e0 di immagini.<\/p>\n<p>Con X-Embodiment, DeepMind ha creato un set di dati di azioni robotiche basate su 22 tipi diversi di robot. Ha poi utilizzato questo set di dati per addestrare nuovi modelli basati sui suoi modelli robotici RT-1 e RT-2.<\/p>\n<p>I dati di X-Embodiment sono stati ricavati da \"22 incarnazioni di robot, che hanno dimostrato pi\u00f9 di 500 abilit\u00e0 e 150.000 compiti in pi\u00f9 di 1 milione di episodi\", secondo quanto riportato da <a href=\"https:\/\/www.deepmind.com\/blog\/scaling-up-learning-across-many-different-robot-types\" target=\"_blank\" rel=\"noopener\">Il post di DeepMind<\/a>.<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\" style=\"text-align: center;\">Vi presentiamo \ud835\udde5\ud835\udde7-\ud835\uddeb: un modello di IA generalista che aiuta a far progredire il modo in cui i robot possono imparare nuove abilit\u00e0. \ud83e\udd16<\/p>\n<p>Per addestrarlo, abbiamo collaborato con 33 laboratori accademici di tutto il mondo per costruire un nuovo set di dati con le esperienze acquisite da 22 diversi tipi di robot.<\/p>\n<p>Per saperne di pi\u00f9: <a href=\"https:\/\/t.co\/k6tE62gQGP\">https:\/\/t.co\/k6tE62gQGP<\/a> <a href=\"https:\/\/t.co\/IXTy2g4Lty\">pic.twitter.com\/IXTy2g4Lty<\/a><\/p>\n<p style=\"text-align: center;\">- Google DeepMind (@GoogleDeepMind) <a href=\"https:\/\/twitter.com\/GoogleDeepMind\/status\/1709207886943965648?ref_src=twsrc%5Etfw\">3 ottobre 2023<\/a><\/p>\n<\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<p>I precedenti risultati dei test dell'RT-1 e <a href=\"https:\/\/dailyai.com\/it\/2023\/07\/googles-ai-turns-vision-language-into-robotic-actions\/\">Modelli RT-2<\/a> erano gi\u00e0 notevoli, ma DeepMind ha scoperto che le versioni RT-X hanno ottenuto risultati significativamente migliori grazie alla natura generale del nuovo set di dati.<\/p>\n<p>I test prevedevano il confronto tra un robot controllato da un modello addestrato per un compito specifico e lo stesso robot controllato dal modello RT-1-X. RT-1-X ha ottenuto in media 50% in pi\u00f9 rispetto ai modelli progettati specificamente per compiti come l'apertura di una porta o il passaggio di un cavo.<\/p>\n<p>RT-2, il modello robotico VLA (vision-language-action) di Google, consente ai robot di imparare dai dati web, verbali e visivi e di agire senza essere addestrati. Quando gli ingegneri hanno addestrato RT-2-X con il set di dati X-Embodiment, hanno scoperto che RT-2-X aveva un successo tre volte superiore a quello di RT-2 per quanto riguarda le abilit\u00e0 emergenti.<\/p>\n<figure id=\"attachment_6135\" aria-describedby=\"caption-attachment-6135\" style=\"width: 640px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-6135 size-full\" src=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/RT-2-X-demonstration.gif\" alt=\"\" width=\"640\" height=\"360\" \/><figcaption id=\"caption-attachment-6135\" class=\"wp-caption-text\">RT-2-X dimostra di comprendere le relazioni spaziali tra gli oggetti. Fonte: <a href=\"https:\/\/www.deepmind.com\/blog\/scaling-up-learning-across-many-different-robot-types\" target=\"_blank\" rel=\"noopener\">DeepMind<\/a><\/figcaption><\/figure>\n<p>In altre parole, il robot stava imparando nuove abilit\u00e0 che non aveva prima, basandosi sulle capacit\u00e0 che altri robot avevano contribuito al set di dati. Il trasferimento di competenze tra diversi tipi di robot potrebbe essere una svolta per lo sviluppo rapido della robotica.<\/p>\n<p>Questi risultati sono motivo di ottimismo: presto vedremo robot con competenze pi\u00f9 generali e con la capacit\u00e0 di apprenderne di nuove senza essere specificamente addestrati.<\/p>\n<p>DeepMind sostiene che questa ricerca potrebbe essere applicata alla propriet\u00e0 di auto-miglioramento di <a href=\"https:\/\/www.deepmind.com\/blog\/robocat-a-self-improving-robotic-agent\" target=\"_blank\" rel=\"noopener\">RoboCat<\/a>, il suo agente AI automigliorante per la robotica.<\/p>\n<p>La prospettiva di avere un robot che continua a migliorare e ad apprendere nuove competenze sarebbe un enorme vantaggio in campi come la produzione, l'agricoltura o la sanit\u00e0. Queste nuove competenze potrebbero essere applicate anche nel settore <a href=\"https:\/\/dailyai.com\/it\/2023\/10\/darpa-wants-to-use-ai-to-make-better-battlefield-decisions\/\">industria della difesa<\/a> che \u00e8 forse una prospettiva meno attraente, anche se inevitabile.<\/p>","protected":false},"excerpt":{"rendered":"<p>DeepMind di Google ha collaborato con 33 diversi laboratori accademici per creare un set di dati per l'addestramento all'intelligenza artificiale basato su 22 diversi tipi di robot. I robot sono molto bravi a fare una cosa specifica. Se si vuole che facciano qualcosa di leggermente diverso, il robot deve essere addestrato da zero. L'obiettivo finale della robotica \u00e8 avere un robot che sia bravo in una gamma generale di azioni e che sia in grado di apprendere da solo nuove abilit\u00e0. Per addestrare un modello di intelligenza artificiale \u00e8 necessario un ampio insieme di dati relativi allo scopo del modello. I modelli linguistici come il GPT-4 vengono addestrati<\/p>","protected":false},"author":6,"featured_media":6136,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[147,102,105,169],"class_list":["post-6133","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-deepmind","tag-google","tag-machine-learning","tag-robotics"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Open X-Embodiment dataset, RT-X model a leap for AI robots | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/it\/2023\/10\/open-x-embodiment-dataset-rt-x-model-a-leap-for-ai-robots\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Open X-Embodiment dataset, RT-X model a leap for AI robots | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Google\u2019s DeepMind worked with 33 different academic labs to create an AI training dataset based on 22 different robot types. 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