Katie Washabaugh To build and deploy sophisticated robotics applications that can perceive, reason and act in dynamic environments, developers need new physical AI models and tools. The ROS open framework is a project from Open Robotics that helps humans build robots. NVIDIA Isaac ROS 5.0 — a collection of GPU-accelerated packages built on ROS, released today at […]
To build and deploy sophisticated robotics applications that can perceive, reason and act in dynamic environments, developers need new physical AI models and tools. The ROS open framework is a project from Open Robotics that helps humans build robots. …
Robotics is entering a new phase: moving from controlled demos and scripted automation toward generalizable, reliable embodied autonomy in the real world. At the International Conference on Robotics and Automation (ICRA), eight of NVIDIA Research’s 28…
Robotics is entering a new phase: moving from controlled demos and scripted automation toward generalizable, reliable embodied autonomy in the real world. At the International Conference on Robotics and Automation (ICRA), eight of NVIDIA Research’s 28 accepted papers show how simulation-to-real transfer is becoming a foundation for that shift, helping robots perceive, reason, plan and […]
Robotics is entering a new phase: moving from controlled demos and scripted automation toward generalizable, reliable embodied autonomy in the real world. At the International Conference on Robotics and Automation (ICRA), eight of NVIDIA Research’s 28 accepted papers show how simulation-to-real transfer is becoming a foundation for that shift, helping robots perceive, reason, plan and […]
The latest open models and frameworks from NVIDIA bring together simulation, robot learning and embedded compute to accelerate cloud-to-robot workflows.
The latest open models and frameworks from NVIDIA bring together simulation, robot learning and embedded compute to accelerate cloud-to-robot workflows.
Editor’s note: This post is part of Into the Omniverse, a series focused on how developers, 3D practitioners and enterprises can transform their workflows using the latest advancements in OpenUSD and NVIDIA Omniverse. Physical AI is moving from researc…
Editor’s note: This post is part of Into the Omniverse, a series focused on how developers, 3D practitioners and enterprises can transform their workflows using the latest advancements in OpenUSD and NVIDIA Omniverse. Physical AI is moving from research labs into the real world, powering intelligent robots and autonomous vehicles (AVs) — such as robotaxis
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Physical AI models — which power robots, autonomous vehicles and other intelligent machines — must be safe, generalized for dynamic scenarios and capable of perceiving, reasoning and operating in real time.
Physical AI models — which power robots, autonomous vehicles and other intelligent machines — must be safe, generalized for dynamic scenarios and capable of perceiving, reasoning and operating in real time.
Simulated driving environments enable engineers to safely and efficiently train, test and validate autonomous vehicles (AVs) across countless real-world and edge-case scenarios without the risks and costs of physical testing.
Simulated driving environments enable engineers to safely and efficiently train, test and validate autonomous vehicles (AVs) across countless real-world and edge-case scenarios without the risks and costs of physical testing.
Autonomous vehicle (AV) stacks are evolving from many distinct models to a unified, end-to-end architecture that executes driving actions directly from sensor data. This transition to using larger models is drastically increasing the demand for high-quality, physically based sensor data for training, testing and validation. To help accelerate the development of next-generation AV architectures, NVIDIA
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Autonomous vehicle (AV) stacks are evolving from many distinct models to a unified, end-to-end architecture that executes driving actions directly from sensor data. This transition to using larger models is drastically increasing the demand for high-qu…
Teaching autonomous robots and vehicles how to interact with the physical world requires vast amounts of high-quality data. To give researchers and developers a head start, NVIDIA is releasing a massive, open-source dataset for building the next genera…
Teaching autonomous robots and vehicles how to interact with the physical world requires vast amounts of high-quality data. To give researchers and developers a head start, NVIDIA is releasing a massive, open-source dataset for building the next generation of physical AI. Announced at NVIDIA GTC, a global AI conference taking place this week in San
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Generative AI and foundation models let autonomous machines generalize beyond the operational design domains on which they’ve been trained. Using new AI techniques such as tokenization and large language and diffusion models, developers and researchers…
Generative AI and foundation models let autonomous machines generalize beyond the operational design domains on which they’ve been trained. Using new AI techniques such as tokenization and large language and diffusion models, developers and researchers can now address longstanding hurdles to autonomy. These larger models require massive amounts of diverse data for training, fine-tuning and
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The path to safe, widespread autonomous vehicles is going digital. MITRE — a government-sponsored nonprofit research organization — today announced its partnership with Mcity at the University of Michigan to develop a virtual and physical autonomous ve…