How to Build a Low-Latency Robot Teleoperation System for ML Data Collection

Researchers and developers training robot policies via imitation learning rely heavily on the quality of teleoperation systems used to gather demonstration data. A well-designed system requires low and consistent latency, high-frequency synchronized logging, smooth continuous control, and repeatable operator inputs. The typical stack is structured in four layers: input device, mapping/retargeting, robot controller, and a synchronized data logger. Common input devices range from leader-follower arm pairs and SpaceMouse controllers to gamepads and VR controllers, with leader-follower and VR setups generally producing the cleanest data for manipulation tasks. The tutorial also provides a minimal Python control loop framework to help developers implement their own teleoperation sessions at a configurable control frequency.