Photorealistic synthetic data and novel rendering techniques significantly advanced computer vision research. However, datasets focused on computer vision applications cannot be easily applied to robotics because they typically lack physics-related information. This, combined with the difficulties of realistically simulating dynamic worlds and the insufficient photorealism, flexibility, and control options of common robotics simulation frameworks, hinders progress in (visual-)perception research for autonomous robotics. For instance, most Visual Simultaneous Localization and Mapping methods are passive, developed under a (semi-)static environment assumption, and evaluated on just a limited number of pre-recorded datasets. To address these challenges, we present a highly customizable framework built upon NVIDIA Isaac Sim for Generating Realistic and Dynamic Environments—GRADE. GRADE leverages Isaac’s rendering capabilities, physics engine, and low-level APIs to populate and manage realistic simulations, generate synthetic data, and evaluate online and offline robotics approaches, including Active SLAM and heterogeneous multi-robot scenarios. Within GRADE, we int
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