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About

Zichang Guo is a robotics and intelligent control researcher whose work bridges reinforcement learning, computer vision, and soft materials engineering. His research primarily focuses on solving fundamental challenges in robotic manipulation, particularly inverse kinematics for arm robots and adaptive grasping for irregular objects. Guo's most impactful contribution is a reinforcement learning approach for inverse kinematics that overcomes the limitations of traditional analytical and numerical methods, offering a more efficient solution for complex robot structures. This work has garnered 20 citations. He further advanced the field with a distributed reward algorithm for inverse kinematics (11 citations) and applied deep learning to robotic grasping, using a modified YOLO algorithm to detect optimal grasping positions for irregular objects (15 citations). In a notable interdisciplinary achievement, Guo contributed to "Inverse programming of ferromagnetic domains for 3D curved surfaces of soft materials" (2025, 14 citations), demonstrating his versatility beyond traditional robotics. His earlier work on disturbance rejection control for two-wheeled self-balancing robots on uneven pavement rounds out a career dedicated to making robots more autonomous, adaptable, and capable in real-world environments.