Nvidia wants to be the Android of generalist robotics | TechCrunch
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Nvidia wants to be the Android of generalist robotics | TechCrunch
"Nvidia released a new stack of robot foundation models, simulation tools, and edge hardware at CES 2026, moves that signal the company's ambition to become the default platform for generalist robotics, much as Android became the operating system for smartphones. Nvidia's move into robotics reflects a broader industry shift as AI moves off the cloud and into machines that can learn how to think in the physical world, enabled by cheaper sensors, advanced simulation, and AI models that increasingly can generalize across tasks."
"Those models include: Cosmos Transfer 2.5 and Cosmos Predict 2.5, two world models for synthetic data generation and robot policy evaluation in simulation; Cosmos Reason 2, a reasoning vision language model (VLM) that allows AI systems to see, understand, and act in the physical world; and Isaac GR00T N1.6, its next-gen vision language action (VLA) model purpose-built for humanoid robots."
"Nvidia also introduced Isaac Lab-Arena at CES, an open-source simulation framework hosted on GitHub that serves as another component of the company's physical AI platform, enabling safe virtual testing of robotic capabilities. The platform promises to address a critical industry challenge: as robots learn increasingly complex tasks, from precise object handling to cable installation, validating these abilities in physical environments can be costly, slow, and risky."
Nvidia launched a comprehensive physical AI ecosystem comprising open foundation models, simulation frameworks, and edge hardware to enable generalist robotics. The stack includes Cosmos Transfer 2.5 and Cosmos Predict 2.5 for synthetic data and policy evaluation, Cosmos Reason 2 as a reasoning vision-language model, and Isaac GR00T N1.6 for whole-body humanoid control. Models are available on Hugging Face and integrate with Isaac Lab-Arena, an open-source simulation framework on GitHub. The platform consolidates resources, benchmarks like Libero, RoboCasa, and RoboTwin, and training tools to reduce the cost, time, and risk of validating complex robotic behaviors in the physical world.
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