Teleoperation is real-time control of a robot by a human through an input device. In robot learning it is primarily a data-collection method: the operator performs the task through the robot, and the robot's observations and commanded actions are recorded as demonstrations for imitation learning. Demonstration quality bounds what a policy can learn. The GELLO paper (Wu et al., 2023) states it directly: "the performance of the learned policies is bottlenecked by the quality, scale, and variety of the demonstration data".
Interfaces differ in how the operator's input maps to the robot. Joint-space devices, chiefly a leader arm, command each joint directly and need no inverse kinematics. Task-space devices such as VR controllers, 3D mice and phones command an end-effector pose that must be converted to joint targets. The ALOHA system in Zhao et al. (2023) used joint-space mapping and recorded at 50 Hz.
In LeRobot v0.6.1, lerobot-teleoperate drives a robot from a teleoperator such as so101_leader, and lerobot-record writes episodes to a LeRobotDataset. The resulting dataset is the training set for policies such as ACT and SmolVLA.
The teleoperation interface determines the action space in the dataset. A leader-arm recording on an SO-101 stores joint-position targets; an end-effector recording does not, and a policy trained on one cannot be run on the other without conversion. Teleoperation rate matters too: in the ACT paper's user study, lowering the teleoperation frequency from 50 Hz to 5 Hz made human operators 62% slower.