{"id":19,"date":"2026-05-05T23:20:00","date_gmt":"2026-05-05T23:20:00","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf9\/?page_id=19"},"modified":"2026-05-07T07:54:34","modified_gmt":"2026-05-07T07:54:34","slug":"introduction","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf9\/introduction\/","title":{"rendered":"Introduction"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/www.contrib.andrew.cmu.edu\/~pangchil\/mscv\/spring\/introduction.png\" alt=\"\" \/><\/figure>\n\n\n\n<p>Robotic manipulation is increasingly driven by Vision\u2013Language\u2013Action (VLA) models, which connect perception, language, and action. However, these models are difficult to scale because real-world robot data is expensive to collect, time-consuming to annotate, and often unavailable to the broader community. As a result, VLA models can overfit to limited training environments and require additional fine-tuning when deployed in new settings.<\/p>\n\n\n\n<p>This project explores simulation as a scalable alternative. We focus on reconstructing real-world scenes into simulation-ready 3D environments, aligning them with robot and camera viewpoints, and using them as a foundation for synthetic data generation, benchmarking, reward learning, and future VLA training.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why Simulation<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Challenges in Robot Learning<\/h3>\n\n\n\n<p>Real-world teleoperation is expensive and difficult to scale. Large-scale robot datasets are often not publicly accessible, and VLA models can overfit to narrow training distributions. As a result, deploying policies in new environments often requires significant additional adaptation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Our Direction<\/h3>\n\n\n\n<p>We investigate a simulation-centered pipeline that reconstructs real-world scenes into simulation, builds simulation-ready 3D backgrounds and assets, and supports scalable benchmarking, synthetic data generation, and reward learning.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">From Real Scenes to Simulation-Ready Worlds<\/h2>\n\n\n\n<p>A central part of this project is reconstructing real-world scenes inside simulation. Starting from visual input, we explore ways to recover complete 3D backgrounds using video generation, video-to-world reconstruction, and image-to-world models. The reconstructed background is then aligned with the simulated robot and camera setup through pose and scale optimization, producing an explorable 3D environment that can support robot-centered evaluation and learning.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Robotic manipulation is increasingly driven by Vision\u2013Language\u2013Action (VLA) models, which connect perception, language, and action. However, these models are difficult to scale because real-world robot data is expensive to collect, time-consuming to annotate, and often unavailable to the broader community. As a result, VLA models can overfit to limited training environments and require additional fine-tuning [&hellip;]<\/p>\n","protected":false},"author":284,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-19","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>Introduction - Scaling Vision\u2013Language\u2013Action Models for Robotic Manipulation through Simulation<\/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\/2026teamf9\/introduction\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Introduction - Scaling Vision\u2013Language\u2013Action Models for Robotic Manipulation through Simulation\" \/>\n<meta property=\"og:description\" content=\"Robotic manipulation is increasingly driven by Vision\u2013Language\u2013Action (VLA) models, which connect perception, language, and action. 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