In teleoperation a human steers a robot from a distance, for example by moving a matching set of “leader” arms that the robot’s arms copy. Every camera frame and joint movement can be recorded.
Those recordings are the main training data for robot imitation learning, and collecting them is slow and costly, which is why robot data is scarce. Stanford’s ALOHA project reported a two-arm teleoperation system costing under $20,000.