Yuxiang Fu

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About

Yuxiang Fu is a robotics researcher whose work bridges the critical gap between human intention and robotic action, with a particular focus on adaptive human-robot interaction and advanced control theory. His most cited work, "Adaptive Multi-Task Human-Robot Interaction Based on Human Behavioral Intention" (2021, 11 citations), addresses a fundamental limitation in robot skill learning by moving beyond traditional Probabilistic Movement Primitives (ProMPs). While standard ProMPs treat each task independently, Fu’s research introduces a multi-task framework that adapts to human behavioral cues, enabling more fluid and intuitive collaboration between humans and robots. This work is foundational for creating robots that can seamlessly adjust to varying tasks in real-time. In his more recent contribution, "Rotational Impedance Formulation in a Unified Viewpoint of Lie Algebra" (2025, 3 citations), Fu tackles the persistent inconsistency in how rotational impedance is modeled in robotic manipulation. By leveraging Lie algebra, he provides a mathematically rigorous, unified formulation that enhances safety and stability during contact-rich tasks. Fu’s research is particularly impactful for the development of next-generation collaborative robots, where understanding and predicting human intent is as crucial as precise mechanical control.