Physical AI at a glance

Physical AI is the field of building models that perceive and act in the real, physical world — robots that walk, grasp, balance, and recover. Unlike a chatbot, a physical-AI policy has to close the loop with physics: it observes the world, acts, and feels the consequences, thousands of times a second.

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Why it’s hard

The bottleneck in physical AI is not model architecture — it’s data.

• Real-world data is expensive. Every trajectory needs a physical robot, an operator, and time. Robots break, batteries die, and a single hour of teleop yields very few labeled examples.
• Mistakes are costly. A policy that fails on hardware can damage the robot or its surroundings, so you can’t just let it explore freely in the real world.
• Coverage is thin. Rare events — slips, recoveries, edge-case terrain — are exactly what you need to train on, and exactly what you almost never capture.

A modern vision-language-action (VLA) policy wants millions of diverse, well-labeled trajectories. Collecting those by hand on real hardware does not scale.

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Simulation closes the gap

Cadenza’s answer is high-fidelity simulation as a data engine. Inside a MuJoCo-based physics core you can:

• Run thousands of missions in parallel, far faster than real time.
• Generate rare and dangerous scenarios on demand, with no hardware at risk.
• Log every tick — observations, actions, contacts, rewards — automatically.
• Score each rollout so the data arrives already labeled.

The result is a steady stream of clean, diverse trajectories that you can feed straight into fine-tuning.

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Closing the sim-to-real loop

Simulation is only useful if the policy survives contact with reality. Cadenza keeps the simulated loop and the deployed loop identical, so the policy you validated in sim is the one that runs on the robot — and real-world rollouts can flow back in as data, tightening the loop with every deployment.