معرفی
Xiaoli Zhang serves as Associate Professor in the Department of Mechanical Engineering at Colorado School of Mines, where she leads cutting-edge research in human-robot cooperation, intelligent control systems, and teleoperation with applications spanning healthcare, surgery, additive manufacturing, and underground construction. Her work focuses on enhancing robot adaptability and robustness through optimal control, machine learning, and cognitive science principles, supported by facilities like the Intelligent Robotics and Systems Lab.
Dr. Zhang's research integrates artificial intelligence with robotics to solve complex engineering challenges, particularly in smart human-machine interaction and shared autonomy systems. Key areas include natural-language-based robot control, gaze-driven assistance interfaces, and physics-informed machine learning for additive manufacturing processes. Her methodologies emphasize real-world applicability in medical robotics and industrial automation, where systems must dynamically adapt to environmental changes and human collaborators.
Analysis of her recent publications reveals dominant trends in machine learning-enhanced robotics, with significant contributions to additive manufacturing quality control, dexterous manipulation, and safety-conscious teleoperation systems. Her work consistently bridges theoretical AI advancements with practical engineering applications, particularly in laser-based manufacturing and assistive robotics where human-robot synergy is critical.
Scientific recognition includes:
- NSF CAREER award for pioneering research in robotics and intelligent systems
Dr. Zhang directs the Intelligent Robotics and Systems Lab, which develops advanced control frameworks for human-robot teams while securing competitive funding like the NSF CAREER grant. Her educational impact extends through courses such as MEGN 545 Advanced Robot Control and MEGN 441 Introduction to Robotics, training next-generation engineers in autonomous systems design. The lab maintains strong industry partnerships for translating research into surgical robotics and manufacturing applications.
The Intelligent Robotics and Systems Lab operates as a multidisciplinary hub where computer vision, machine learning, and mechanical engineering converge to solve real-world problems. Current projects focus on gaze-based control systems for surgical assistance, transfer learning for multi-platform additive manufacturing, and adaptive shared autonomy frameworks that maintain safety during human-robot collaboration in dynamic environments.
