Max Robotics — Global Service Robot Directory + US Certification

About

Xu Xie is a leading researcher at the intersection of human-robot interaction, explainable AI, and autonomous systems. Her work fundamentally addresses how robots can earn human trust, predict human behavior, and operate intelligently in the real world. In her highly cited paper "A Tale of Two Explanations" (132 citations), she pioneered a framework for enhancing human trust by enabling robots to explain their actions in comprehensible ways—a critical step toward deploying AI in high-stakes environments. Her influential research on trajectory prediction, particularly the Latent Belief Energy-Based Model (LB-EBM) (84 citations), provides probabilistic methods for forecasting human motion, essential for safe self-driving cars and social robots. Xie also innovates in interactive robot learning, using augmented reality (73 citations) to diagnose and patch robot knowledge, and integrating force and pose sensing (66 citations) for dexterous manipulation tasks like opening medicine bottles. Her work on reconstructing interactive 3D scenes (30 citations) and the VRGym testbed (25 citations) pushes boundaries in creating functional, actionable environments for embodied agents. Through these contributions, Xie has established herself as a pivotal figure in making robots more trustworthy, capable, and seamlessly integrated into human spaces.