This chapter addresses the challenge of accurately mapping and controlling a robot based on the operator’s body movements using a teleoperation system. The key issue lies in translating human body posture and intent into robot movements, considering the differences between human bodies and robots. Traditional methods improve tracking accuracy by increasing control frequency, but they can lead to uneven robot speeds and jerky motion. This study uses ABB’s dual-arm collaborative robot, YuMi, and Noitom Perception Neuron (PN) motion capture device to track the operator’s upper limb movements. The design involves a single-arm human pose mapping control interface and a distributed communication system between the robot and PN device. The primary challenge addressed is ensuring smooth and accurate end trajectory tracking despite low-frequency pose data output. To solve this, the chapter proposes a local path resampling algorithm. This method improves the tracking accuracy and trajectory similarity between the operator and robot, ensuring continuous trajectory mapping and tracking. Comparative experiments with varying resampling frequencies demonstrate that the best trajectory tracking occurs at a specific resampling rate, with minimal tracking errors in both simple (1.05 mm) and complex (5.10 mm) trajectories.