{"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\/pt\/2024\/01\/google-releases-a-suite-of-advanced-robotic-tools\/","title":{"rendered":"A Google lan\u00e7a um conjunto de ferramentas rob\u00f3ticas avan\u00e7adas"},"content":{"rendered":"<p><strong>A Google DeepMind lan\u00e7ou um conjunto de novas ferramentas para ajudar os rob\u00f4s a aprender de forma aut\u00f3noma, mais r\u00e1pida e eficazmente, em ambientes novos.<\/strong><\/p>\n<p>Treinar um rob\u00f4 para executar uma tarefa espec\u00edfica num \u00fanico ambiente \u00e9 uma tarefa de engenharia relativamente simples. Para que os rob\u00f4s nos sejam verdadeiramente \u00fateis no futuro, ter\u00e3o de ser capazes de executar uma s\u00e9rie de tarefas gerais e aprender a faz\u00ea-las em ambientes que nunca experimentaram antes.<\/p>\n<p>No ano passado, a DeepMind lan\u00e7ou o seu <a href=\"https:\/\/dailyai.com\/pt\/2023\/10\/open-x-embodiment-dataset-rt-x-model-a-leap-for-ai-robots\/\">Modelo de controlo rob\u00f3tico RT-2<\/a> e RT-X. O RT-2 traduz comandos de voz ou de texto em ac\u00e7\u00f5es rob\u00f3ticas.<\/p>\n<p>As novas ferramentas anunciadas pela DeepMind baseiam-se no RT-2 e aproximam-nos de rob\u00f4s aut\u00f3nomos que exploram diferentes ambientes e aprendem novas compet\u00eancias.<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">Nos \u00faltimos dois anos, os modelos de grandes funda\u00e7\u00f5es provaram ser capazes de perceber e raciocinar sobre o mundo que nos rodeia, abrindo uma possibilidade fundamental para a rob\u00f3tica de escala.<\/p>\n<p>Apresentamos o AutoRT, uma estrutura para orquestrar agentes rob\u00f3ticos na natureza usando modelos de funda\u00e7\u00e3o! <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 janeiro 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>O AutoRT combina um Modelo de Linguagem Grande (LLM) fundamental com um Modelo de Linguagem Visual (VLM) e um modelo de controlo de rob\u00f4s como o RT-2.<\/p>\n<p>O VLM permite ao rob\u00f4 avaliar o cen\u00e1rio \u00e0 sua frente e transmitir a descri\u00e7\u00e3o ao LLM. O LLM avalia os objectos identificados e a cena e gera uma lista de potenciais tarefas que o rob\u00f4 pode realizar.<\/p>\n<p>As tarefas s\u00e3o avaliadas com base na sua seguran\u00e7a, nas capacidades do rob\u00f4 e no facto de a realiza\u00e7\u00e3o da tarefa acrescentar ou n\u00e3o novas compet\u00eancias ou diversidade \u00e0 base de conhecimentos do 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\u00e1lise ambiental do AutoRT e processo de sele\u00e7\u00e3o de tarefas. Fonte: <a href=\"https:\/\/deepmind.google\/discover\/blog\/shaping-the-future-of-advanced-robotics\/\" target=\"_blank\" rel=\"noopener\">DeepMind<\/a><\/figcaption><\/figure>\n<p>A DeepMind afirma que com o AutoRT \"orquestrou em seguran\u00e7a at\u00e9 20 rob\u00f4s em simult\u00e2neo, e at\u00e9 52 rob\u00f4s \u00fanicos no total, numa variedade de edif\u00edcios de escrit\u00f3rios, reunindo um conjunto de dados diversificado que inclui 77 000 ensaios rob\u00f3ticos em 6650 tarefas \u00fanicas\".<\/p>\n<h2>Constitui\u00e7\u00e3o rob\u00f3tica<\/h2>\n<p>Enviar um rob\u00f4 para novos ambientes significa que este ir\u00e1 encontrar situa\u00e7\u00f5es potencialmente perigosas que n\u00e3o podem ser planeadas especificamente. Ao utilizar uma constitui\u00e7\u00e3o rob\u00f3tica como guia de orienta\u00e7\u00e3o, os rob\u00f4s recebem uma prote\u00e7\u00e3o de seguran\u00e7a generalizada.<\/p>\n<p>A constitui\u00e7\u00e3o rob\u00f3tica \u00e9 inspirada nas 3 leis da rob\u00f3tica de Isaac Asimov:<\/p>\n<ol>\n<li>Um rob\u00f4 n\u00e3o pode ferir um ser humano.<\/li>\n<li>Este rob\u00f4 n\u00e3o deve tentar realizar tarefas que envolvam seres humanos, animais ou seres vivos. Este rob\u00f4 n\u00e3o deve interagir com objectos afiados, como uma faca.<\/li>\n<li>Este rob\u00f4 s\u00f3 tem um bra\u00e7o, pelo que n\u00e3o pode executar tarefas que exijam dois bra\u00e7os. Por exemplo, n\u00e3o consegue abrir uma garrafa.<\/li>\n<\/ol>\n<p>O cumprimento destas directrizes evita que o rob\u00f4 seleccione uma tarefa da lista de op\u00e7\u00f5es que possa magoar algu\u00e9m ou danificar-se a si pr\u00f3prio ou a outra coisa.<\/p>\n<h2>SARA-RT<\/h2>\n<p>O SARA-RT (Self-Adaptive Robust Attention for Robotics Transformers) pega em modelos como o RT-2 e torna-os mais eficientes.<\/p>\n<p>A arquitetura da rede neural do RT-2 assenta em m\u00f3dulos de aten\u00e7\u00e3o de complexidade quadr\u00e1tica. Isto significa que se duplicarmos a entrada, adicionando um novo sensor ou aumentando a resolu\u00e7\u00e3o da c\u00e2mara, precisamos de quatro vezes mais recursos computacionais.<\/p>\n<p>O SARA-RT utiliza um modelo de aten\u00e7\u00e3o linear para afinar o modelo rob\u00f3tico. Isto resultou numa melhoria de 14% na velocidade e em ganhos de precis\u00e3o de 10%.<\/p>\n<h2>RT-Trajet\u00f3ria<\/h2>\n<p>Converter uma tarefa simples, como limpar uma mesa, em instru\u00e7\u00f5es que um rob\u00f4 possa seguir \u00e9 complicado. A tarefa tem de ser convertida da linguagem natural para uma sequ\u00eancia codificada de movimentos e rota\u00e7\u00f5es do motor para acionar as pe\u00e7as m\u00f3veis do rob\u00f4.<\/p>\n<p>O RT-Trajectory adiciona uma sobreposi\u00e7\u00e3o visual 2D a um v\u00eddeo de treino para que o rob\u00f4 possa aprender intuitivamente que tipo de movimento \u00e9 necess\u00e1rio para realizar a tarefa.<\/p>\n<p>Assim, em vez de apenas dar instru\u00e7\u00f5es ao rob\u00f4 para \"limpar a mesa\", a demonstra\u00e7\u00e3o e a sobreposi\u00e7\u00e3o de movimentos d\u00e3o-lhe uma melhor hip\u00f3tese de aprender rapidamente a nova compet\u00eancia.<\/p>\n<p>A DeepMind afirma que um bra\u00e7o controlado pelo RT-Trajectory \"alcan\u00e7ou uma taxa de sucesso de 63%, em compara\u00e7\u00e3o com 29% para o RT-2\".<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">Tamb\u00e9m pode criar traject\u00f3rias observando demonstra\u00e7\u00f5es humanas, compreendendo esbo\u00e7os e at\u00e9 desenhos gerados por VLM.<\/p>\n<p>Quando testado em 41 tarefas n\u00e3o vistas nos dados de treino, um bra\u00e7o controlado pelo RT-Trajectory obteve uma taxa de sucesso 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 janeiro de 2024<\/a><\/p><\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<p>A DeepMind est\u00e1 a disponibilizar estes modelos e conjuntos de dados a outros programadores, pelo que ser\u00e1 interessante ver como estas novas ferramentas aceleram a integra\u00e7\u00e3o de rob\u00f4s alimentados por IA na vida quotidiana.<\/p>\n<p>&nbsp;<\/p>","protected":false},"excerpt":{"rendered":"<p>A Google DeepMind lan\u00e7ou um conjunto de novas ferramentas para ajudar os rob\u00f4s a aprender de forma aut\u00f3noma, mais r\u00e1pida e eficazmente, em ambientes novos. Treinar um rob\u00f4 para executar uma tarefa espec\u00edfica num \u00fanico ambiente \u00e9 uma tarefa de engenharia relativamente simples. Para que os rob\u00f4s nos possam ser verdadeiramente \u00fateis no futuro, ter\u00e3o de ser capazes de executar uma s\u00e9rie de tarefas gerais e aprender a faz\u00ea-las em ambientes que nunca experimentaram antes. No ano passado, a DeepMind lan\u00e7ou o seu modelo de controlo rob\u00f3tico RT-2 e os conjuntos de dados rob\u00f3ticos RT-X. O RT-2 traduz comandos de voz ou de texto em ac\u00e7\u00f5es rob\u00f3ticas. As novas ferramentas<\/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\/pt\/2024\/01\/google-releases-a-suite-of-advanced-robotic-tools\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\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. Training a robot to perform a specific task in a single environment is a relatively simple engineering task. If robots are going to be truly useful to us in the future they\u2019ll need to be able to perform a range of general tasks and learn to do them in environments that they\u2019ve not experienced before. Last year DeepMind released its RT-2 robotics control model and RT-X robotic datasets. RT-2 translates voice or text commands into robotic actions. 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