In traditional systems, teaching a robot to flip a pancake with a spatula might involve countless hours of training on a specific arm model. But what if you wanted the same task done by a different robot with different sensors or arms?
Overcoming Heterogeneity within CYBER allows for cross-embodiment learning—the robot learns fundamental skills that are transferable across different platforms. Picture a scenario where a robot trained in a factory on one set of machinery can seamlessly adapt to a new set of machines at a different facility. CYBER provides human-captured data and teleoperation data to provide a foundation for cross-embodiment learning, avoiding the need to train each robot from scratch for every new task or embodiment.
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