{"id":3315,"date":"2023-07-29T11:38:41","date_gmt":"2023-07-29T11:38:41","guid":{"rendered":"https:\/\/dailyai.com\/?p=3315"},"modified":"2023-07-29T11:38:41","modified_gmt":"2023-07-29T11:38:41","slug":"googles-ai-turns-vision-language-into-robotic-actions","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nl\/2023\/07\/googles-ai-turns-vision-language-into-robotic-actions\/","title":{"rendered":"Google's AI zet visie en taal om in robotacties"},"content":{"rendered":"<p><strong>Google heeft een aantal spannende testresultaten laten zien van zijn nieuwste vision-language-action (VLA) robotmodel genaamd Robotics Transformer 2 (RT-2).<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Het grootste deel van de recente AI-discussies draait om grote taalmodellen zoals ChatGPT en Llama. De antwoorden die deze modellen geven zijn weliswaar nuttig, maar blijven op het scherm van je apparaat staan. Met RT-2 brengt Google de kracht van AI naar de fysieke wereld. Een wereld waarin zelflerende robots binnenkort deel kunnen uitmaken van ons dagelijks leven.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">De behendigheid van robots is sterk verbeterd, maar ze hebben nog steeds zeer specifieke programmeerinstructies nodig om zelfs eenvoudige taken uit te voeren. Als de taak verandert, al is het maar een klein beetje, moet het programma worden aangepast.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Met RT-2 heeft Google een model gemaakt waarmee een robot kan classificeren en leren van de dingen die hij ziet in combinatie met woorden die hij hoort. Vervolgens redeneert hij op basis van de instructies die hij krijgt en onderneemt hij fysieke actie als antwoord.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Met LLM's wordt een zin opgedeeld in tokens, in wezen hapklare brokken van woorden waarmee de AI de zin kan begrijpen. Google heeft dat principe gebruikt om de bewegingen die een robot zou moeten maken als reactie op een commando te tokenen.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">De bewegingen van een robotarm met een grijper worden bijvoorbeeld opgesplitst in tokens van veranderingen in x- en y-posities of rotaties.<\/span><\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\" style=\"text-align: center;\">In het verleden hadden robots meestal ervaring uit eerste hand nodig om een actie uit te voeren. Maar met ons nieuwe vision-taal-actiemodel, RT-2, kunnen ze nu leren van zowel tekst als afbeeldingen van het web om nieuwe en complexe taken uit te voeren. Meer informatie \u2193 <a href=\"https:\/\/t.co\/4DSRwUHhwg\">https:\/\/t.co\/4DSRwUHhwg<\/a><\/p>\n<p style=\"text-align: center;\">- Google (@Google) <a href=\"https:\/\/twitter.com\/Google\/status\/1684974085837660170?ref_src=twsrc%5Etfw\">28 juli 2023<\/a><\/p>\n<\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<h2><span style=\"font-weight: 400;\">Wat kan een robot met RT-2?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Omdat de robot kan begrijpen wat hij ziet en hoort en kan redeneren in een gedachteketen, hoeft hij niet geprogrammeerd te worden voor nieuwe taken.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Een voorbeeld dat Google gaf in zijn DeepMind <\/span><a href=\"https:\/\/www.deepmind.com\/blog\/rt-2-new-model-translates-vision-and-language-into-action\"><span style=\"font-weight: 400;\">blogbericht over RT-2<\/span><\/a><span style=\"font-weight: 400;\"> was \"beslissen welk voorwerp kan worden gebruikt als ge\u00efmproviseerde hamer (een steen), of welk soort drankje het beste is voor een vermoeid persoon (een energiedrankje).\"<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In de tests die Google uitvoerde, onderwierp het een robotarm en grijper aan een reeks verzoeken die taalbegrip, visie en redeneren vereisten om de juiste actie te kunnen ondernemen. Als de robot bijvoorbeeld 2 zakken chips op een tafel zag liggen, waarvan er \u00e9\u00e9n iets over de rand hing, kreeg hij de opdracht om \"de zak op te pakken die van de tafel dreigde te vallen\".<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Dat klinkt misschien eenvoudig, maar het contextuele bewustzijn dat nodig is om de juiste tas op te pakken is baanbrekend in de wereld van de robotica.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Om uit te leggen hoeveel geavanceerder RT-2 is dan gewone LLM's wordt in een ander Google-blog uitgelegd dat \"een robot een appel in de context moet kunnen herkennen, deze moet kunnen onderscheiden van een rode bal, moet begrijpen hoe de appel eruitziet en vooral moet weten hoe hij de appel moet oppakken.\"<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Hoewel het nog vroeg is, is het vooruitzicht van huishoudelijke of industri\u00eble robots die helpen met allerlei taken in veranderende omgevingen opwindend. De defensietoepassingen krijgen vrijwel zeker ook aandacht.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">De robotarm van Google deed het niet altijd goed en had een grote rode noodstopknop voor het geval hij niet goed werkte. Laten we hopen dat de toekomstige robots iets soortgelijks hebben voor het geval ze op een dag niet tevreden zijn met de baas.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>Google heeft een aantal spannende testresultaten laten zien van zijn nieuwste vision-taal-actie (VLA) robotmodel genaamd Robotics Transformer 2 (RT-2). Het grootste deel van de recente AI-discussies draait om grote taalmodellen zoals ChatGPT en Llama. De antwoorden die deze modellen geven zijn weliswaar nuttig, maar blijven op het scherm van je apparaat staan. Met RT-2 brengt Google de kracht van AI naar de fysieke wereld. Een wereld waarin zelflerende robots binnenkort deel kunnen uitmaken van ons dagelijks leven. De behendigheid van robots is sterk verbeterd, maar ze hebben nog steeds zeer specifieke programmeerinstructies nodig om zelfs eenvoudige taken uit te voeren. Wanneer<\/p>","protected":false},"author":6,"featured_media":3367,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[147,102,169],"class_list":["post-3315","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-deepmind","tag-google","tag-robotics"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Google\u2019s AI turns vision &amp; language into robotic actions | 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\/nl\/2023\/07\/googles-ai-turns-vision-language-into-robotic-actions\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Google\u2019s AI turns vision &amp; language into robotic actions | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Google showcased some exciting test results of its latest vision-language-action (VLA) robot model called Robotics Transformer 2 (RT-2). The bulk of recent AI discussions has centered around large language models like ChatGPT and Llama. The responses these models provide, while useful, remain on the screen of your device. With RT-2, Google is bringing the power of AI to the physical world. A world where self-learning robots could soon be a part of our everyday lives. There has been a big improvement in the dexterity of robots but they still need very specific programming instructions to accomplish even simple tasks. 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