L. Hafemeister

École Nationale Supérieure de l'Électronique et de ses Applications, Centre National de la Recherche Scientifique

Papers

Total Citations

H-Index

About

L. Hafemeister explores the frontiers of developmental robotics and cognitive science, focusing on how agents—both biological and artificial—learn through imitation, perception, and sensorimotor interaction. Their most influential work, "Imitation as a communication tool for online facial expression learning and recognition" (2010, 28 citations), tackles a fundamental puzzle: how infants learn to recognize emotions like sadness or happiness without explicit teaching signals. By modeling imitation as a communicative bridge, Hafemeister offers a groundbreaking framework for unsupervised learning in social robots. Earlier contributions, such as "Perception as a Dynamical Sensori-Motor Attraction Basin" (2005, 24 citations), reframe perception not as passive input but as an active, dynamic process shaped by movement and context. This work, alongside "Proprioception and Imitation: On the Road to Agent Individuation" (2009, 10 citations), advances understanding of how agents develop a sense of self through embodied interaction. In "A context and task dependent visual attention system to control a mobile robot" (2003, 10 citations), Hafemeister designed an artificial neural network for context-driven visual attention, enabling robots to navigate unknown environments autonomously. With a career spanning foundational theory and applied robotics, their research continues to inspire new approaches to machine learning, social cognition, and autonomous systems.