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About

Ganyu Deng is a leading roboticist specializing in quadruped locomotion, force control, and energy-efficient robot design. His research focuses on enabling legged robots—particularly the Pegasus series—to traverse challenging, unstructured terrains with both agility and endurance. Deng’s most influential work introduces a novel method for joint torque estimation in gear-driven systems, allowing dynamic and compliant control without torque sensors, a breakthrough for robust interaction with rough ground. His papers on foothold selection and posture optimization, which have garnered over 70 combined citations, demonstrate how convolutional neural networks and ZMP-based planners can dramatically improve locomotion efficiency. Notably, Deng’s bionic architecture for the high-payload Pegasus robot, inspired by canine anatomy, has set a new standard for balancing strength and compliance in quadrupedal platforms. His vision-based navigation system for the Pegasus-Mini further bridges the gap between blind robust walking and autonomous real-world deployment. With a growing citation record and a clear trajectory from foundational control theory to applied field robotics, Deng’s work is essential reading for anyone advancing legged locomotion toward practical, energy-savvy autonomy.