Max Robotics — Global Service Robot Directory + US Certification

About

Xiaotian Chen is a rising researcher in the rapidly evolving field of soft robotics, with a focused expertise in the modeling, control, and fault detection of soft trunk robots. Chen’s major contributions lie in addressing the fundamental challenge of accurately modeling the complex, nonlinear dynamics of soft robots—a critical barrier to their real-world deployment. By pioneering data-driven, adaptive learning approaches, particularly using radial basis function neural networks (RBF NN), Chen has developed novel frameworks for both dynamics learning and tracking control. A standout achievement is the creation of a generic, learning-based fault isolation method, enabling soft robots to detect and diagnose internal failures autonomously, a significant step toward reliable autonomous operation. With a growing body of highly cited work from 2022-2023—including papers garnering 16 and 14 citations in top venues—Chen’s research is at the forefront of making soft robots not just flexible, but intelligent and resilient. Their work is essential reading for anyone interested in the intersection of machine learning, control theory, and next-generation robotic systems.