Mobile ALOHA

Whole-body teleoperation: A human operator simultaneously controls two arms and a wheeled base through a teleop rig, producing naturally coordinated mobile-manipulation demonstrations.

Behavior cloning + co-training: Supervised behavior cloning on ~50 demonstrations per task, co-trained with existing static ALOHA datasets, dramatically improves generalization on complex mobile tasks.

Under $32K: The hardware budget stays under $32K, making the platform accessible to academic and small-lab research - a key reason the release went viral.

Hard real-world tasks: Demonstrates sauteing and serving shrimp, opening a two-door cabinet to store heavy pots, and other multi-step mobile manipulation tasks previously thought to require far more data or compute.