Notes on physical AI & synthetic data
Notes on physical AI and synthetic data from datadoo experts: engineers, data scientists, and our research team.
Robots Are Shipping. Training Data Is Not.
Humanoids are deploying, $6B flowed into physical AI in Q1 2026, and the bottleneck has shifted from hardware to training data. Physics-accurate synthetic data is the binding constraint.
The EU AI Act's Article 10 Is an Argument for Synthetic Data
Article 10 of the EU AI Act demands training data that is representative, complete, and free of errors. Real-world data rarely meets that bar. Synthetic data does.
Digital Twins for Training: How We Build Simulation Environments in Omniverse
A technical walkthrough of how datadoo builds Digital Twins in NVIDIA Omniverse and uses them to generate physics-accurate training data at scale.
What We Took Away from GTC 2026
We presented our research on synthetic windshield damage detection at GTC 2026. Here is what we learned, what we heard on the floor, and why Physical AI is moving faster than anyone expected.
Why Physical AI Starts with Synthetic Data
Physical AI systems need training data that obeys the laws of physics. Real-world capture cannot provide it at the scale, speed, or safety required. Synthetic data can.
Grounded Intelligence: How World Models Can Bridge Today's AI and Physical AI
World models learn to simulate physical dynamics. They may be the missing link between today's language-centric AI and the embodied systems that need to act in the real world.
The Synthetic Data Trap: When More Data Makes Your Model Worse
Generating millions of synthetic images is easy. Generating the right ones is hard. We break down the distribution mismatch problem and how to avoid it.
Physical AI Needs Physical Truth: Synthetic Data That Obeys the World
Most synthetic data pipelines optimize for visual fidelity. For physical AI, that is necessary but not sufficient. The underlying physics must be accurate too.
How Data Shapes AI Behavior: A Synthetic Perspective
Training data is not a passive input to model training. It is the primary lever for controlling what a model learns, how it fails, and who it works for.