Robotics Simulation

Programming robots for real-world success requires a training process that accounts for unpredictable conditions, different surfaces, variations in object size,...

From humanoids to policy, explore the work NVIDIA is bringing to the robotics community.

Large-scale, use–case-specific synthetic data has become increasingly important in real-world computer vision and AI workflows. That’s because digital twins...

The era of AI robots powered by physical AI has arrived. Physical AI models understand their environments and autonomously complete complex tasks in the...

This post is the first in a series on building multi-camera tracking vision AI applications. In this part, we introduce the overall end-to-end workflow,...

Autonomous machine development is an iterative process of data generation and gathering, model training, and deployment characterized by complex multi-stage,...

Robots are typically equipped with cameras. When designing a digital twin simulation, it’s important to replicate its performance in a simulated environment...