Research & Insights

All posts and news from the Physicl team.

Introducing Physicl: The Data Layer for Physical AI

Physical AI is data-limited, not model-limited. We built Physicl to solve that. Here's why we started, what we're building, and what we're opening up today.

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June 16, 2026

Embodied AI in 2026: The Race to Teach AI How to Interact with the Physical World

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

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Physical AI

June 2, 2026

What Is a Digital Twin — And How It Feeds Physical AI Training

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.

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Physical AI

April 16, 2026

Simulation-Ready 3D Assets: What They Are and Why Most Libraries Don’t Have Them

Simulation-ready 3D assets aren’t just geometry — they require physics properties, correct collision meshes, USD compatibility, and verified simulator behavior. Here’s what the bar actually looks like.

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Research

April 9, 2026

World Model Training Data: The IP Problem Nobody Talks About

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.

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Research

April 2, 2026

What Is Physical AI Training Data?

Physical AI training data is structured, physics-accurate 3D data used to train robots and embodied AI systems. Learn what it is, why it's hard to produce, and what simulation-ready actually means.

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Research

Physical AI

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