From Simulation to Reality, Continuously.

Trained from demonstrations, not rules. This is what imitation learning looks like when the data pipeline is built right precise, repeatable, and ready for the real world. But getting simulation data to mean what you think it means is harder than it looks from coordinate frame mismatches to normalization errors that never surface during training. We've documented exactly where these pipelines break and how to catch it before it costs you a training run.

Two arms, one goal — no hand-coding, no scripts. Bimanual coordination has historically been one of the hardest data collection challenges in robotics, requiring synchronized hardware and multiple operators. We solved it in simulation, using spatial computing to let a single operator demonstrate both arms naturally — then scaled it to thousands of training examples automatically.

Autonomous, aware, and always on patrol. Our quadruped navigates complex indoor environments without human intervention trained entirely in simulation before ever stepping onto a real floor.

Eyes in the sky, trained in simulation. Fireloop's SDG pipeline prepares aerial agents for real-world deployment from day one — no physical test environment required.