Physical AI Glossary: 40 Terms for Robot Training Data, Simulation, and 3D Reconstruction

Physical AI glossary presents terminology for the models, datasets, simulations, 3D assets, and control methods used to train robots to perceive and act in real environments, reflecting usage as of October 2026. It distinguishes commonly conflated concepts such as vision-language-action models, robot foundation models, world foundation models, and vision foundation models, explaining their differing roles in perception, planning, and action. Key data concepts include Episodes, which align robot observations, commands, and outcomes over a full task trial, teleoperation for collecting imitation-learning data, and Success Rate as a central measure of task completion. The glossary also covers simulation assets with physical properties, sim-to-real gaps, domain randomization, Gaussian-splatting-based 3D reconstruction, and motion retargeting between humans and robots with different body structures. Each definition links to related practical material from Superb AI, whose Physical AI Data Factory provides 3D, action, and object assets alongside simulated and real-world robot training data.

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