Education

In simulation, a robot runs millions of experiments in about two days — and learns the task instead of being told every step of it. That's how our robots handle the real-world variation of day-to-day work.

The old control stack was hand-tuned loops and handcrafted layers — every exception on the line needed a new patch. Modern manipulation is end to end: raw sensors in, motion out. One learned model absorbs the variation, the same way self-driving replaced a stack of hand-coded classifiers.

The payoff is common sense on the line — robots that handle line-breaking exceptions and spin up quickly, without a three-month, million-dollar project.