How to Build a Robot Teleoperation System for Real-World Data Collection

Researchers and developers working on robot learning often face a data bottleneck, as simulation alone is insufficient and real-world robot data is costly to gather. A teleoperation system offers a practical solution by generating synchronized observation-action pairs that mirror what a deployed policy will encounter during inference. The architecture typically comprises four layers: an input device, a control mapping module, a robot interface, and a data logger. Common input devices range from affordable 3D mice and gamepads to high-fidelity leader-follower arms and VR controllers, each offering different trade-offs between cost and control precision. Proper tuning of scaling factors in the control mapper is critical, as it directly determines how natural and responsive the teleoperation experience feels to the human operator.