{"id":21,"date":"2026-05-08T00:28:43","date_gmt":"2026-05-08T00:28:43","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/?page_id=21"},"modified":"2026-05-08T01:05:40","modified_gmt":"2026-05-08T01:05:40","slug":"background","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/","title":{"rendered":"Background: The 3D Precision Gap"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"401\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/wp-content\/uploads\/sites\/150\/2026\/05\/image-1024x401.png\" alt=\"\" class=\"wp-image-31\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/wp-content\/uploads\/sites\/150\/2026\/05\/image-1024x401.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/wp-content\/uploads\/sites\/150\/2026\/05\/image-300x118.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/wp-content\/uploads\/sites\/150\/2026\/05\/image-768x301.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/wp-content\/uploads\/sites\/150\/2026\/05\/image.png 1297w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><strong>The Shift to 3D Robotics<\/strong> Traditional robotic manipulation has long relied on <strong>2D Vision-Language-Action (VLA)<\/strong> models which, while capable of broad generalization, often lack the <strong>explicit 3D inductive bias<\/strong> required for high-precision manipulation. By incorporating spatial coordinates directly into the model\u2019s internal map, <strong>3D VLAs<\/strong> (like 3DDA and 3DFA) can ground actions in the physical geometry of a scene, leading to significantly better task performance.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"307\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/wp-content\/uploads\/sites\/150\/2026\/05\/Screenshot-2026-05-07-at-9.05.30-PM-1024x307.png\" alt=\"\" class=\"wp-image-42\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/wp-content\/uploads\/sites\/150\/2026\/05\/Screenshot-2026-05-07-at-9.05.30-PM-1024x307.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/wp-content\/uploads\/sites\/150\/2026\/05\/Screenshot-2026-05-07-at-9.05.30-PM-300x90.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/wp-content\/uploads\/sites\/150\/2026\/05\/Screenshot-2026-05-07-at-9.05.30-PM-768x230.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/wp-content\/uploads\/sites\/150\/2026\/05\/Screenshot-2026-05-07-at-9.05.30-PM.png 1194w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><strong>The Calibration Crisis<\/strong> However, 3D policies introduce a critical vulnerability: they depend on <strong>perfect camera extrinsics<\/strong>. In real-world environments, camera mounts suffer from physical drift, vibrations, and sensor remounting. Most large-scale robotic datasets, including <strong>DROID<\/strong> and <strong>Open X-Embodiment<\/strong>, contain significant calibration noise or lack 3D annotations entirely. When a camera\u2019s yaw is off by even a few degrees, the resulting 3D point cloud misaligns with the robot\u2019s physical frame, leading to <strong>catastrophic grasp failures<\/strong> as the model attempts to interact with &#8220;ghost&#8221; objects.<\/p>\n\n\n\n<p><strong>Project Objective<\/strong> Our project aims to bridge this divide by enabling 3D policies to learn from <strong>imperfect data<\/strong>. We develop a system capable of <strong>implicit self-calibration<\/strong> during the forward pass, allowing robots to &#8220;re-center&#8221; their internal coordinate frames without the need for manual recalibration or external targets like checkerboards.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Shift to 3D Robotics Traditional robotic manipulation has long relied on 2D Vision-Language-Action (VLA) models which, while capable of broad generalization, often lack the explicit 3D inductive bias required for high-precision manipulation. By incorporating spatial coordinates directly into the model\u2019s internal map, 3D VLAs (like 3DDA and 3DFA) can ground actions in the physical [&hellip;]<\/p>\n","protected":false},"author":283,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-21","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Background: The 3D Precision Gap - Learning 3D-VLAs with Noisy Miscalibrated Data<\/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:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Background: The 3D Precision Gap - Learning 3D-VLAs with Noisy Miscalibrated Data\" \/>\n<meta property=\"og:description\" content=\"The Shift to 3D Robotics Traditional robotic manipulation has long relied on 2D Vision-Language-Action (VLA) models which, while capable of broad generalization, often lack the explicit 3D inductive bias required for high-precision manipulation. 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