{"product_id":"9781394319206","title":"Artificial Intelligence: From Simulation to Reality","description":"\u003ch1\u003eArtificial Intelligence: From Simulation to Reality\u003c\/h1\u003e \u003ch2\u003eVelasquez, Alvaro; Patel, Vishal; Loquercio, Antonio; Fern, Alan\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTransfer simulation-trained AI to real-world robotic platforms effectively\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eTraining AI in simulation offers efficiency and safety advantages, but deploying that intelligence on physical platforms introduces challenges that can undermine performance. \u003ci\u003eArtificial Intelligence: From Simulation to Reality\u003c\/i\u003e addresses this critical gap directly. Compiled by researchers from DARPA, Johns Hopkins, Penn, and Oregon State, this volume provides the methodologies needed to successfully transfer simulated learning to real-world autonomous systems. \u003c\/p\u003e\u003cp\u003eThe book covers diverse simulation environments , AI techniques for sim-to-real transfer, and a variety of exciting and relevant application domains, including autonomous vehicle drifting, bipedal locomotion, control of humanoid robots, human-in-the loop robotics, quadruped autonomy, and superhuman drone racing. This book also presents a modern treatment of classical concepts in robotics, including how large language models and vision-language-action model training techniques can be adapted to train robots in simulation for real-world transfer. Each chapter addresses specific sim-to-real challenges with proven solutions. \u003c\/p\u003e\u003cp\u003eReaders will also explore: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eCoverage of multiple simulation platforms, environments, and techniques enabling practitioners to select the right tools for their specific robotics applications\u003c\/li\u003e \u003cli\u003eDomain randomization and system dynamics techniques that improve the robustness of AI models when transitioning from simulated to physical environments\u003c\/li\u003e \u003cli\u003eMachine learning methods for predicting robot trajectories that account for real-world uncertainties absent from idealized simulation training scenarios\u003c\/li\u003e \u003cli\u003eTechniques adapted from large language model development showing how transformer-based approaches can enhance sim-to-real transfer for autonomous robots\u003c\/li\u003e \u003cli\u003ePractical guidance on addressing the quintessential challenges that arise when deploying simulation-trained intelligence on real autonomous platforms\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eResearch scientists, applied scientists, and engineers working in AI, machine learning, or robotics will find this an authoritative resource for sim-to-real transfer. Professors teaching robotics, transfer learning, reinforcement learning, or AI for control courses will find material suitable for advanced undergraduate and graduate curricula.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Wiley-IEEE Press\u003c\/p\u003e \u003cp\u003ePublication Date: 2027-03-02\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9781394319206\u003c\/p\u003e \u003cp\u003eDOI: \u003c\/p\u003e \u003cp\u003eDimensions: cm xcm\u003c\/p\u003e \u003cp\u003ePages: \u003c\/p\u003e ","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47348143423628,"sku":"9781394319206","price":126.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781394319206_ad94e64c-9710-48ff-b1a7-db782e758f98.jpg?v=1776341673","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9781394319206","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}