Building realistic 3D environments for robotics simulation has traditionally been a labor-intensive process, often requiring weeks of manual modeling and setup....

Building realistic 3D environments for robotics simulation has traditionally been a labor-intensive process, often requiring weeks of manual modeling and setup. Now, with generative world models, you can go from a text prompt to a photorealistic, simulation-ready world in a fraction of time. By combining NVIDIA Isaac Sim, an open source robotics reference framework, with generative models such as…

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Building powerful physical AI models requires diverse, controllable, and physically-grounded data at scale. Collecting large-scale, diverse real-world datasets...

Building powerful physical AI models requires diverse, controllable, and physically-grounded data at scale. Collecting large-scale, diverse real-world datasets for training can be expensive, time-intensive, and dangerous. NVIDIA Cosmos open world foundation models (WFMs) address these challenges by enabling scalable, high-fidelity synthetic data generation for physical AI and the augmentation of…

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Building autonomous robots requires robust, low-latency visual perception for depth, obstacle recognition, localization, and navigation in dynamic environments....

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