Lyumanshan Ye

Papers

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About

Lyumanshan Ye is a pioneering researcher in human-robot interaction and knowledge representation, with a focus on making service robots more accessible to non-expert users. His most cited work, "Patterns for Representing Knowledge Graphs to Communicate Situational Knowledge of Service Robots" (2021, 9 citations), addresses a critical gap in robotics: while knowledge graphs are powerful tools for encoding a robot's situational awareness, their visual representations are often designed solely for technical experts. Ye's key contribution lies in identifying and formalizing design patterns that translate complex graph structures into intuitive interfaces, enabling everyday users to understand and trust a robot's decision-making process. This work bridges the gap between advanced AI reasoning and human-centered design, laying the groundwork for more transparent and collaborative human-robot teams. Though early in his career, Ye's research has already shaped discussions on explainable AI in robotics, and his patterns serve as a foundational reference for developers seeking to democratize robot programming. His ongoing work continues to explore how visual knowledge communication can empower non-experts to interact seamlessly with autonomous systems in dynamic environments.