Embodied AI systems learn from interaction. To teach a robot how to pick, place, assemble, sort, or use tools, researchers need demonstration data that describes not only the final result, but also the sequence of movements that produced it.

This becomes especially challenging in dual-arm and dexterous manipulation. The operator must coordinate two arms, adjust joint positions continuously, and often control individual fingers at the same time. Conventional controllers can operate a robot, but they may require the user to translate a natural human action into buttons, joysticks, or separate commands. Video can record what happened, yet it does not directly provide complete joint-level motion data.

The ExoArm-7 Pro wearable exoskeleton is designed to address this gap by capturing human arm movements directly and turning them into high-frequency data for robot teleoperation, imitation learning, embodied AI, and VLA development.