Our trial-and-error learning approach uses simulated data and parallel GPU simulations to train legged robots efficiently. Overcoming sim-to-real challenges, we’ve developed proprietary learning algorithms and game-like training worlds that closely mimic reality. This enables our robots to master mobility, manipulation, and autonomy tasks in just days.

We leverage real-world data, similar to autonomous driving, to enhance our robots' ability to interpret complex scenes from camera images. By collecting expert data through remote intervention, we train them to navigate and respond autonomously.