Moving Atoms

Companies building Physical AI and robots need realistic simulated worlds, training data, and evaluation methods to teach robots skills and verify they work before deployment. Capturing accurate digital representations of real-world sites and objects, then turning them into physically accurate, simulation-ready assets, is a specialized and time-consuming task. Without reproducible benchmarks, teams also have no standardized way to measure a robot policy's progress or compare it against alternatives. Robot developers need an ingestion process that brings a specific robot and task into simulation, including control interfaces and system identification, before any training can begin.

Moving Atoms offers a three-layer data engine: a Task & World Layer, a Skill Creation Harness, and a Deployment & Evaluation Harness. The World Layer includes a SimReady Library of physically accurate assets and a Capture Layer (Scan to Model) that converts a LiDAR or SLAM scan into separated, measured, editable objects in formats like IFC, Revit, STEP, USD or GLB within 48 hours. The Skill Creation Harness lets teams compose modular skills, train policies, demonstrate via human teleop, or transfer skills from video into simulation. RobotGym, the training layer, is a browser-based platform to build worlds and run training and evaluation, offered with $100 in welcome credits. An open Evaluation Benchmark tests a robot's progress on 100 randomly selected simulation tasks with frozen scenes, seeds and budgets, allowing reproducible, independent scoring or head-to-head policy comparisons.