Insights.

The six things to check in synthetic data for computer vision, from exact ground truth to realism, and how public datasets, in-house and platforms compare.

Objaverse is a library of shapes, not simulation assets. Why they fail in a physics engine, and what Physicl provides instead. Request access

LiDAR simulation returns believable, wrong point clouds when 3D assets carry no material data. Physicl ships physics-tagged assets. Request access

The public scene datasets for AI Habitat are 211 homes. Physicl builds custom, physics-accurate Habitat datasets for your domain. Request access

Synthetic data powers AI training at scale. Learn what it is, how it works, and why physical AI requires a fundamentally different approach to generate it.

Most Isaac Sim assets look right and simulate wrong. Physicl builds physics-accurate, USD-ready assets for Isaac Sim. Request access

Explore embodied AI in 2026, from simulation training environments and robotics data challenges to real-world applications in autonomous systems.

A digital twin is a physics-accurate virtual replica of a real-world asset. Learn how they work, how they're built, and why Physical AI teams train on them.

Simulation-ready 3D assets need more than geometry: physics properties, correct collision meshes, USD compatibility and verified behavior in the simulator.

Training world models on 3D spatial data has a problem most teams don’t surface until it’s too late: IP licensing. Here’s why provenance matters as much as scale.

Physical AI training data is physics-accurate 3D data used to train robots and embodied AI. What it is, why it's hard to produce, and what sim-ready means.