Open Source · Nvidia · Hugging Face
The primary challenge in building physical AI systems is data availability
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Large language models (LLMs) and vision-language models (VLMs) can be trained on internet-scale datasets, but robotics and physical AI systems do not have the same advantage.
Key facts
- With Isaac Lab 3.0.0
- The team have decoupled the Isaac Sim and omniverse dependency, and made Isaac Lab a lightweight, multi backend Robot learning framework
- NVIDIA Isaac Lab 3.0 is an open-source, GPU-accelerated, agent-ready simulation framework for robot learning, designed to train and evaluate robot policies at scale
- Figure 4: The Newton Physics Engine is an open-source, GPU-accelerated simulation engine built upon NVIDIA Warp, designed for roboticists and simulation researchers
- Newton is an open-source, GPU-accelerated, extensible, and differentiable physics engine developed by NVIDIA, Google DeepMind, and Disney Research, and managed through the Linux Foundation
Summary
Figure 1: Humanoid robot locomotion simulation. The primary challenge in building physical AI systems is data availability. Collecting this kind of data in the real world is slow, expensive, risky, and sometimes impractical due to the destructive nature of the tasks. Earlier robotics simulators were often used primarily to debug geometry, test controllers, or visualize robot motion. Training computer: A large GPU cluster used to process data and train foundation AI models.