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About

Claas-Norman Ritter is a robotics researcher whose work bridges the gap between biological perception and artificial agency. His primary research areas include robot ego-noise modelling, multimodal body representations, and cooperative perception for autonomous systems. Ritter’s most significant contribution lies in developing biologically inspired models that allow robots to learn and predict their own noise patterns—a crucial step toward giving machines a sense of agency. His 2016 paper on body representations for ego-noise modelling, which has accumulated 34 citations across two versions, demonstrates how robots can use multimodal sensory feedback to distinguish self-generated sounds from environmental noise, enabling more adaptive and self-aware behavior. More recently, Ritter has ventured into cooperative autonomous driving, with his 2023 work on Cooperative LiDAR Localization and Mapping (C-SLAM) for V2X-connected vehicles—already cited 7 times—addressing the challenge of real-time collaborative mapping for connected autonomous vehicles. This work represents a promising direction for safer, more efficient autonomous transportation. Ritter’s research elegantly combines insights from neuroscience, machine learning, and robotics, making him a notable figure in the development of perceptive, self-aware artificial agents.