Abstract—In the modern era, Robotics has become an essential part of modern life, enhancing efficiency and precision in manufacturing and supporting diagnosis, prediction, and surgery in medicine. This paper presents a real-time teleoperation framework that maps human upper-body motion, captured by a single RGB-D camera, to a dual-arm upper humanoid robot designed with low-cost servos. The system employs MediaPipe-based pose estimation and a torso-anchored coordinate transformation to achieve operator-centric retargeting that is robust to variations in camera placement and subject geometry. To suppress tremor and sensor noise, a constant-velocity Kalman filter combined with an adaptive dead-zone is applied to wrist trajectories, ensuring smooth motion while maintaining responsiveness. A min-max scaling function with saturation enforces safe workspace mapping, while joint commands are computed using damped-least-squares inverse kinematics with joint-limit and self-collision checks. The execution layer incorporates incremental speed-aware stepping to emulate continuous trajectories on servo actuators. Experimental results demonstrate accurate static pose reproduction, robust dynamic path following, and zero joint-limit violations, achieving an average wrist-tracking Root Mean Square Error (RMSE) of 12.4 mm and median end-to-end latency of 86 ms. The platform is reproducible, cost-effective, and adaptable for applications in education, rehabilitation, and human–robot collaboration.

Keywords—humanoid robotics, teleoperation, human-robot interaction, robot kinematics, motion capture system, image processing, signal processing, Kalman filters

Cite: Omar Salem, A. Abdellatif, and Mostafa R. A. Atia, "Pose Estimation-Driven Control of Humanoid Upper Arms for Human Motion Mimicry," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 2, pp. 174-186, 2026. doi: 10.18178/ijmerr.15.2.174-186

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).