Max Robotics — Global Service Robot Directory + US Certification

About

Zhiguang Liu is a leading researcher in human-robot interaction and autonomous navigation, whose work bridges the gap between robotic perception and collaborative intelligence. His primary contributions lie in intention recognition for physical human-robot collaboration, where he developed a method using radial basis function neural networks (RBFNN) to enable robots to anticipate a human partner’s motion during haptic tasks—solving the critical synchronization problem in cooperative manipulation. This work, published in 2019, has garnered 26 citations and is foundational for intuitive robot assistance. Liu also advanced sensorless contact estimation for planar robots, proposing a real-time technique to infer contact forces without external sensors, a key enabler for safer, more affordable interaction systems (9 citations). Most recently, he has tackled path planning for mobile robots in rough terrain, introducing an improved A* algorithm that integrates ground trafficability and ruggedness models to ensure stability and optimality—a notable achievement for field robotics (7 citations, 2024). Through these contributions, Liu has shaped how robots understand human intent and navigate challenging environments, with his work cited across robotics and control engineering.