Benjamin Cohen-Lhyver

Centre National de la Recherche Scientifique, Institut Systèmes Intelligents et de Robotique, Sorbonne Université

Papers

Total Citations

H-Index

About

Benjamin Cohen-Lhyver is a researcher at the intersection of developmental robotics, active perception, and intrinsically motivated learning. His work centers on how autonomous agents can use auditory cues and multimodal feedback to spontaneously explore unknown environments—a paradigm shift from efficiency-driven exploration to curiosity-driven behavior. Cohen-Lhyver’s most cited paper, “Audition as a Trigger of Head Movements” (2020, 7 citations), demonstrates how sound can initiate orienting behaviors, laying groundwork for more lifelike robotic attention. His earlier “The Head Turning Modulation System” (2018, 5 citations) proposes an active multimodal framework where robots are motivated to explore not by external commands but by internal drives—a key contribution to the growing field of intrinsic motivation in robotics. In “Modulating the auditory turn-to reflex” (2015, 4 citations), he introduces the Dynamic Weighting model (DWmod), a low-level attention algorithm that modulates spontaneous head movements based on real-time sensory feedback loops. Though his citation counts are modest, Cohen-Lhyver’s work is notable for its conceptual originality: by treating audition as a trigger for exploratory action, he helps bridge the gap between sensorimotor reflexes and higher-level cognitive exploration, offering a fresh perspective for students and researchers interested in embodied AI and developmental systems.