Max Robotics — Global Service Robot Directory + US Certification

About

Sibo Yang’s research lies at the intersection of human-robot interaction, assistive robotics, and machine learning, with a focus on enabling intuitive control of upper-limb assistive devices. His major contributions center on motion-intention prediction and real-time control for rehabilitation and assistive robots. By integrating multi-modal wearable sensors—such as inertial measurement units and electromyography—with advanced machine learning techniques, Yang has developed models that predict joint angles, end-point positions, and motion trajectories, allowing robots to respond proactively to user intent. His work on learning-based controllers and adaptive gravity compensation frameworks directly addresses the challenge of natural, active human-robot collaboration. Notably, his most-cited paper, “Elbow Motion Trajectory Prediction Using a Multi-Modal Wearable System” (27 citations), demonstrates the effectiveness of comparing machine learning techniques for upper-limb motion prediction. Yang’s earlier work on neural network calibration for robotic joints (17 citations) and his development of a microfabricated dual slip-pressure sensor (10 citations) highlight a sustained commitment to improving robotic sensing and control. With over 80 total citations, Yang’s research is shaping the future of assistive robotics, making rehabilitation more accessible and intuitive for patients with limb impairments.