{"id":5480,"date":"2025-05-17T05:47:45","date_gmt":"2025-05-16T21:47:45","guid":{"rendered":"https:\/\/cicserver.com\/new-lego-building-ai-creates-models-that-actually-stand-up-in-real-life\/"},"modified":"2025-05-17T05:47:45","modified_gmt":"2025-05-16T21:47:45","slug":"new-lego-building-ai-creates-models-that-actually-stand-up-in-real-life","status":"publish","type":"post","link":"https:\/\/cicserver.com\/de\/new-lego-building-ai-creates-models-that-actually-stand-up-in-real-life\/","title":{"rendered":"New Lego-building AI creates models that actually stand up in real life"},"content":{"rendered":"<p><br \/>\n<\/p>\n<div>\n<figure class=\"ars-wp-img-shortcode id-2094297 align-center\">\n<div>\n                        <img loading=\"lazy\" width=\"1024\" height=\"459\" src=\"https:\/\/cdn.arstechnica.net\/wp-content\/uploads\/2025\/05\/pipeline-1024x459.jpg\" class=\"center large\" alt=\"The LegoGPT system works in three parts, shown in this diagram.\" decoding=\"async\" srcset=\"https:\/\/cdn.arstechnica.net\/wp-content\/uploads\/2025\/05\/pipeline-1024x459.jpg 1024w, https:\/\/cdn.arstechnica.net\/wp-content\/uploads\/2025\/05\/pipeline-640x287.jpg 640w, https:\/\/cdn.arstechnica.net\/wp-content\/uploads\/2025\/05\/pipeline-768x344.jpg 768w, https:\/\/cdn.arstechnica.net\/wp-content\/uploads\/2025\/05\/pipeline-1536x689.jpg 1536w, https:\/\/cdn.arstechnica.net\/wp-content\/uploads\/2025\/05\/pipeline-2048x918.jpg 2048w, https:\/\/cdn.arstechnica.net\/wp-content\/uploads\/2025\/05\/pipeline-980x439.jpg 980w, https:\/\/cdn.arstechnica.net\/wp-content\/uploads\/2025\/05\/pipeline-1440x646.jpg 1440w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\"\/>\n                  <\/div><figcaption>\n<div class=\"caption font-impact dusk:text-gray-300 mb-4 mt-2 inline-flex flex-row items-stretch gap-1 text-base leading-tight text-gray-400 dark:text-gray-300\">\n<div class=\"caption-content\">\n      The LegoGPT system works in three parts, shown in this diagram.<\/p>\n<p>              <span class=\"caption-credit mt-2 text-xs\"><br \/>\n          Credit:<\/p>\n<p>                      <a class=\"caption-credit-link text-gray-400 no-underline hover:text-gray-500\" href=\"https:\/\/avalovelace1.github.io\/LegoGPT\/\" target=\"_blank\"><\/p>\n<p>          Pun et al.<\/p>\n<p>                      <\/a><br \/>\n                  <\/span>\n          <\/div>\n<\/p><\/div>\n<\/figcaption><\/figure>\n<p>The researchers also expanded the system&#8217;s abilities by adding texture and color options. For example, using an appearance prompt like &#8220;Electric guitar in metallic purple,&#8221; LegoGPT can generate a guitar model, with bricks assigned a purple color.<\/p>\n<h2>Testing with robots and humans<\/h2>\n<p>To prove their designs worked in real life, the researchers had robots assemble the AI-created Lego models. They used a dual-robot arm system with force sensors to pick up and place bricks according to the AI-generated instructions.<\/p>\n<p>Human testers also built some of the designs by hand, showing that the AI creates genuinely buildable models. &#8220;Our experiments show that LegoGPT produces stable, diverse, and aesthetically pleasing Lego designs that align closely with the input text prompts,&#8221; the team noted in its paper.<\/p>\n<p>When tested against other AI systems for 3D creation, LegoGPT stands out through its focus on structural integrity. The team tested against several alternatives, including <a href=\"https:\/\/research.nvidia.com\/labs\/toronto-ai\/LLaMA-Mesh\/\">LLaMA-Mesh<\/a> and other 3D-generation models, and found its approach produced the highest percentage of stable structures.<\/p>\n<figure class=\"video ars-wp-video\">\n<p>\n    <video class=\"wp-video-shortcode absolute w-full h-full object-cover left-0 top-0\" id=\"video-2094193-2\" width=\"1280\" height=\"720\" preload=\"metadata\" controls=\"controls\"><source type=\"video\/mp4\" src=\"https:\/\/cdn.arstechnica.net\/wp-content\/uploads\/2025\/05\/guitar_8x.mp4?_=2\"\/>A video of two robot arms building a LegoGPT creation, provided by the researchers.<\/video>\n  <\/p><figcaption>\n    <span class=\"icon caption-arrow icon-drop-indicator\"\/><\/p>\n<div class=\"caption font-impact dusk:text-gray-300 mb-4 mt-2 inline-flex flex-row items-stretch gap-1 text-base leading-tight text-gray-400 dark:text-gray-300\">\n<p>\n      A video of two robot arms building a LegoGPT creation, provided by the researchers.<\/p>\n<\/p><\/div>\n<\/figcaption><\/figure>\n<p>Still, there are some limitations. The current version of LegoGPT only works within a 20\u00d720\u00d720 building space and uses a mere eight standard brick types. &#8220;Our method currently supports a fixed set of commonly used Lego bricks,&#8221; the team acknowledged. &#8220;In future work, we plan to expand the brick library to include a broader range of dimensions and brick types, such as slopes and tiles.&#8221;<\/p>\n<p>The researchers also hope to scale up their training dataset to include more objects than the 21 categories currently available. Meanwhile, others can literally build on their work\u2014the researchers released their dataset, code, and models on their <a href=\"https:\/\/avalovelace1.github.io\/LegoGPT\/\">project website<\/a> and <a href=\"https:\/\/github.com\/AvaLovelace1\/LegoGPT\/\">GitHub<\/a>.<\/p>\n<\/p><\/div>","protected":false},"excerpt":{"rendered":"<p>The LegoGPT system works in three parts, shown in this diagram. Credit: Pun et al. The researchers also expanded the system&#8217;s abilities by adding texture and color options. For example, using an appearance prompt like &#8220;Electric guitar in metallic purple,&#8221; LegoGPT can generate a guitar model, with bricks assigned a purple color. Testing with robots [&hellip;]<\/p>","protected":false},"author":3,"featured_media":5481,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-5480","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-blog"},"_links":{"self":[{"href":"https:\/\/cicserver.com\/de\/wp-json\/wp\/v2\/posts\/5480","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cicserver.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cicserver.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cicserver.com\/de\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/cicserver.com\/de\/wp-json\/wp\/v2\/comments?post=5480"}],"version-history":[{"count":0,"href":"https:\/\/cicserver.com\/de\/wp-json\/wp\/v2\/posts\/5480\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cicserver.com\/de\/wp-json\/wp\/v2\/media\/5481"}],"wp:attachment":[{"href":"https:\/\/cicserver.com\/de\/wp-json\/wp\/v2\/media?parent=5480"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cicserver.com\/de\/wp-json\/wp\/v2\/categories?post=5480"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cicserver.com\/de\/wp-json\/wp\/v2\/tags?post=5480"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}