The researchers at Stanford University have developed a low-cost, comprehensive teleoperation system called Mobile ALOHA for gathering data on comprehensive teleoperation. By placing it on a wheeled base, Mobile ALOHA extends the capabilities of the original ALOHA and gains mobility. The researchers used the static ALOHA dataset for imitation learning and achieved good performance in mobile manipulation tasks. This system offers a low-cost and efficient method for data collection, suitable for tasks that require comprehensive teleoperation in everyday scenarios.