Raphael Golombek

Bielefeld University, Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes, Paderborn University

Papers

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About

Raphael Golombek is a robotics researcher whose work focuses on fault detection, self-awareness, and imitation learning in robotic systems. His major contributions lie in developing data-driven approaches for online fault detection and diagnosis, enabling robots to identify failures in real time by monitoring inter-component communication. Golombek’s 2011 paper on this topic has garnered 18 citations, highlighting its influence in the field. He also pioneered the concept of a probabilistic self-awareness model for robotic systems, published in 2010 with 13 citations, which allows robots to detect failures by learning from internal data exchanges and communication dynamics. This work advances the goal of creating more autonomous and reliable robots capable of self-monitoring. Additionally, Golombek explored imitation learning in heterogeneous robot groups, measuring robot similarity to determine the best demonstrator for imitation—a notable step toward collaborative robotics. His research bridges practical fault detection with theoretical self-awareness, making him a key contributor to robust and adaptive robotic systems.