{"id":8905,"date":"2024-01-05T12:35:47","date_gmt":"2024-01-05T12:35:47","guid":{"rendered":"https:\/\/dailyai.com\/?p=8905"},"modified":"2024-01-05T12:41:49","modified_gmt":"2024-01-05T12:41:49","slug":"google-releases-a-suite-of-advanced-robotic-tools","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/01\/google-releases-a-suite-of-advanced-robotic-tools\/","title":{"rendered":"Google lanza un conjunto de herramientas rob\u00f3ticas avanzadas"},"content":{"rendered":"<p><strong>Google DeepMind ha lanzado un conjunto de nuevas herramientas para ayudar a los robots a aprender de forma aut\u00f3noma con mayor rapidez y eficacia en entornos novedosos.<\/strong><\/p>\n<p>Entrenar a un robot para que realice una tarea espec\u00edfica en un \u00fanico entorno es una tarea de ingenier\u00eda relativamente sencilla. Para que los robots nos sean realmente \u00fatiles en el futuro, tendr\u00e1n que ser capaces de realizar una serie de tareas generales y aprender a hacerlas en entornos que no hayan experimentado antes.<\/p>\n<p>El a\u00f1o pasado DeepMind lanz\u00f3 su <a href=\"https:\/\/dailyai.com\/es\/2023\/10\/open-x-embodiment-dataset-rt-x-model-a-leap-for-ai-robots\/\">Modelo de control rob\u00f3tico RT-2<\/a> y RT-X. RT-2 traduce comandos de voz o texto en acciones rob\u00f3ticas.<\/p>\n<p>Las nuevas herramientas anunciadas por DeepMind se basan en RT-2 y nos acercan a robots aut\u00f3nomos que exploran distintos entornos y aprenden nuevas habilidades.<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">En los dos \u00faltimos a\u00f1os, los modelos de grandes cimientos han demostrado ser capaces de percibir y razonar sobre el mundo que nos rodea, lo que abre una posibilidad clave para escalar la rob\u00f3tica.<\/p>\n<p>Presentamos AutoRT, un marco para orquestar agentes rob\u00f3ticos en la naturaleza utilizando modelos de fundamentos. <a href=\"https:\/\/t.co\/x3YdO10kqq\">pic.twitter.com\/x3YdO10kqq<\/a><\/p>\n<p>- Keerthana Gopalakrishnan (@keerthanpg) <a href=\"https:\/\/twitter.com\/keerthanpg\/status\/1742933208419938402?ref_src=twsrc%5Etfw\">4 de enero de 2024<\/a><\/p><\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<h2>AutoRT<\/h2>\n<p>AutoRT combina un Large Language Model (LLM) fundacional con un Visual Language Model (VLM) y un modelo de control de robots como RT-2.<\/p>\n<p>El VLM permite al robot evaluar la escena que tiene delante y pasar la descripci\u00f3n al LLM. El LLM eval\u00faa los objetos identificados y la escena y, a continuaci\u00f3n, genera una lista de tareas potenciales que el robot podr\u00eda realizar.<\/p>\n<p>Las tareas se eval\u00faan en funci\u00f3n de su seguridad, las capacidades del robot y si el intento de la tarea a\u00f1adir\u00eda o no nuevas habilidades o diversidad a la base de conocimientos de AutoRT.<\/p>\n<figure id=\"attachment_8913\" aria-describedby=\"caption-attachment-8913\" style=\"width: 1232px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-8913 size-full\" src=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/01\/AutoRT-example.webp\" alt=\"\" width=\"1232\" height=\"1386\" srcset=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/01\/AutoRT-example.webp 1232w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/01\/AutoRT-example-267x300.webp 267w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/01\/AutoRT-example-910x1024.webp 910w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/01\/AutoRT-example-768x864.webp 768w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/01\/AutoRT-example-370x416.webp 370w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/01\/AutoRT-example-800x900.webp 800w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/01\/AutoRT-example-740x833.webp 740w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/01\/AutoRT-example-20x23.webp 20w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/01\/AutoRT-example-43x48.webp 43w\" sizes=\"auto, (max-width: 1232px) 100vw, 1232px\" \/><figcaption id=\"caption-attachment-8913\" class=\"wp-caption-text\">An\u00e1lisis medioambiental de AutoRT y proceso de selecci\u00f3n de tareas. Fuente: <a href=\"https:\/\/deepmind.google\/discover\/blog\/shaping-the-future-of-advanced-robotics\/\" target=\"_blank\" rel=\"noopener\">DeepMind<\/a><\/figcaption><\/figure>\n<p>DeepMind afirma que con AutoRT \"orquestaron con seguridad hasta 20 robots simult\u00e1neamente, y hasta 52 robots \u00fanicos en total, en una variedad de edificios de oficinas, recopilando un conjunto de datos diverso que comprende 77.000 ensayos rob\u00f3ticos a trav\u00e9s de 6.650 tareas \u00fanicas.\"<\/p>\n<h2>Constituci\u00f3n rob\u00f3tica<\/h2>\n<p>Enviar un robot a entornos nuevos significa que se encontrar\u00e1 con situaciones potencialmente peligrosas que no pueden planificarse de forma espec\u00edfica. Utilizando una constituci\u00f3n rob\u00f3tica como gu\u00eda, los robots disponen de barandillas de seguridad generalizadas.<\/p>\n<p>La constituci\u00f3n rob\u00f3tica se inspira en las 3 leyes de la rob\u00f3tica de Isaac Asimov:<\/p>\n<ol>\n<li>Un robot no puede lesionar a un ser humano.<\/li>\n<li>Este robot no intentar\u00e1 realizar tareas que impliquen a personas, animales o seres vivos. Este robot no interactuar\u00e1 con objetos afilados, como un cuchillo.<\/li>\n<li>Este robot s\u00f3lo tiene un brazo, por lo que no puede realizar tareas que requieran dos brazos. Por ejemplo, no puede abrir una botella.<\/li>\n<\/ol>\n<p>Seguir estas directrices evita que el robot seleccione una tarea de la lista de opciones que pueda herir a alguien o da\u00f1arse a s\u00ed mismo o a otra cosa.<\/p>\n<h2>SARA-RT<\/h2>\n<p>Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT) toma modelos como RT-2 y los hace m\u00e1s eficientes.<\/p>\n<p>La arquitectura de red neuronal de RT-2 se basa en m\u00f3dulos de atenci\u00f3n de complejidad cuadr\u00e1tica. Esto significa que si se duplica la entrada, a\u00f1adiendo un nuevo sensor o aumentando la resoluci\u00f3n de la c\u00e1mara, se necesitan cuatro veces m\u00e1s recursos computacionales.<\/p>\n<p>SARA-RT utiliza un modelo de atenci\u00f3n lineal para ajustar el modelo rob\u00f3tico. El resultado es una mejora de 14% en velocidad y 10% en precisi\u00f3n.<\/p>\n<h2>RT-Trayectoria<\/h2>\n<p>Convertir una tarea sencilla, como limpiar una mesa, en instrucciones que pueda seguir un robot es complicado. Hay que convertir la tarea de lenguaje natural a una secuencia codificada de movimientos y rotaciones del motor para accionar las piezas m\u00f3viles del robot.<\/p>\n<p>RT-Trajectory a\u00f1ade una superposici\u00f3n visual 2D a un v\u00eddeo de entrenamiento para que el robot pueda aprender intuitivamente qu\u00e9 tipo de movimiento es necesario para realizar la tarea.<\/p>\n<p>As\u00ed, en lugar de pedirle al robot que \"limpie la mesa\", la demostraci\u00f3n y la superposici\u00f3n de movimientos le dan m\u00e1s posibilidades de aprender r\u00e1pidamente la nueva habilidad.<\/p>\n<p>DeepMind dice que un brazo controlado por RT-Trajectory \"logr\u00f3 una tasa de \u00e9xito en la tarea de 63%, en comparaci\u00f3n con 29% para RT-2\".<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">\ud83d\udd35 Tambi\u00e9n puede crear trayectorias observando demostraciones humanas, entendiendo bocetos e incluso dibujos generados por VLM.<\/p>\n<p>Cuando se prob\u00f3 en 41 tareas no vistas en los datos de entrenamiento, un brazo controlado por RT-Trajectory alcanz\u00f3 una tasa de \u00e9xito de 63%. <a href=\"https:\/\/t.co\/rqOnzDDMDI\">https:\/\/t.co\/rqOnzDDMDI<\/a> <a href=\"https:\/\/t.co\/bdhi9W5TWi\">pic.twitter.com\/bdhi9W5TWi<\/a><\/p>\n<p>- Google DeepMind (@GoogleDeepMind) <a href=\"https:\/\/twitter.com\/GoogleDeepMind\/status\/1742932249371402519?ref_src=twsrc%5Etfw\">4 de enero de 2024<\/a><\/p><\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<p>DeepMind pone estos modelos y conjuntos de datos a disposici\u00f3n de otros desarrolladores, por lo que ser\u00e1 interesante ver c\u00f3mo estas nuevas herramientas aceleran la integraci\u00f3n de los robots impulsados por IA en la vida cotidiana.<\/p>\n<p>&nbsp;<\/p>","protected":false},"excerpt":{"rendered":"<p>Google DeepMind ha lanzado un conjunto de nuevas herramientas para ayudar a los robots a aprender de forma aut\u00f3noma con mayor rapidez y eficacia en entornos novedosos. Entrenar a un robot para que realice una tarea espec\u00edfica en un \u00fanico entorno es una tarea de ingenier\u00eda relativamente sencilla. Para que los robots nos sean realmente \u00fatiles en el futuro, tendr\u00e1n que ser capaces de realizar una serie de tareas generales y aprender a hacerlas en entornos que no hayan experimentado antes. El a\u00f1o pasado, DeepMind public\u00f3 su modelo de control rob\u00f3tico RT-2 y sus conjuntos de datos rob\u00f3ticos RT-X. RT-2 traduce \u00f3rdenes de voz o texto en acciones rob\u00f3ticas. Las nuevas herramientas<\/p>","protected":false},"author":6,"featured_media":8908,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[147,102,169],"class_list":["post-8905","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 releases a suite of advanced robotic tools | 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\/es\/2024\/01\/google-releases-a-suite-of-advanced-robotic-tools\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Google releases a suite of advanced robotic tools | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Google DeepMind released a suite of new tools to help robots learn autonomously faster and more efficiently in novel environments. 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