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About

Xiaoqian Ren is a robotics researcher whose work sits at the intersection of mobile manipulation, human-robot collaboration, and advanced control systems. With a focus on enabling robots to operate safely and effectively in human-centered environments, Ren has made notable contributions to the integration of perception, planning, and whole-body control for complex robotic platforms. Ren's most cited work (28 citations) introduced an integrated framework combining ORB-SLAM2-based sensing, autonomous navigation, and whole-body control for mobile manipulators driven by series elastic actuators (SEAs), allowing robots to perform dexterous tasks in unknown environments. A complementary study (23 citations) addressed human-robot collaboration by leveraging Gaussian mixture regression to predict human intentions and deploying adaptive fuzzy control to handle the compliance of flexible-jointed robots — a significant step toward safer, more intuitive co-working scenarios. More recently, Ren extended this line of inquiry to two-handed mobile manipulators tackling cart-pushing tasks, pushing the boundaries of what collaborative robots can accomplish in everyday settings. Collectively, Ren's research advances the field's understanding of how perception, planning, and compliant actuation can be unified to bring capable, human-aware robots into real-world environments.