Transfer simulation-trained AI to real-world robotic platforms effectively Training AI in simulation offers efficiency and safety advantages, but deploying that intelligence on physical platforms introduces challenges that can undermine performance. Artificial Intelligence: From Simulation to Reality 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. The 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. Readers will also explore: Coverage of multiple simulation platforms, environments, and techniques enabling practitioners to select the right tools for their specific robotics applications Domain randomization and system dynamics techniques that improve the robustness of AI models when transitioning from simulated to physical environments Machine learning methods for predicting robot trajectories that account for real-world uncertainties absent from idealized simulation training scenarios Techniques adapted from large language model development showing how transformer-based approaches can enhance sim-to-real transfer for autonomous robots Practical guidance on addressing the quintessential challenges that arise when deploying simulation-trained intelligence on real autonomous platforms Research 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.