Luke Guerdan

Papers

Total Citations

H-Index

About

Luke Guerdan is an emerging researcher at the intersection of human-robot interaction, machine learning, and socially aware robotics. His work addresses some of the most pressing challenges in deploying robots in real-world, human-centered environments, with a particular focus on enabling robots to adapt intelligently to the dynamic and nuanced contexts of everyday human life. Guerdan's most notable contribution, "Federated Continual Learning for Socially Aware Robotics" (2023, 7 citations), tackles two critical barriers to widespread robotic adoption: behavioral adaptability and personalization. By leveraging federated continual learning, his approach allows robots to continuously evolve their social behaviors without compromising user privacy — a significant step toward practical, deployable socially assistive robots. His subsequent work, "Causal-HRI: Causal Learning for Human-Robot Interaction" (2024, 4 citations), further advances the field by integrating causal reasoning into robotic perception, helping robots move beyond correlation-based understanding to genuinely comprehend cause-and-effect relationships in human environments. Though early in his career, Guerdan is establishing a distinctive research identity that bridges fundamental machine learning theory with applied robotics challenges. His focus on real-world deployment and human-centered design positions him as a promising voice in the next generation of HRI researchers.