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About

Erwan Renaudo is a robotics and artificial intelligence researcher whose work sits at the intersection of cognitive architectures, autonomous behavior, and robot learning. Best known for his influential 2018 paper "Toward Self-Aware Robots" (99 citations), Renaudo has consistently challenged the field to move beyond reactive, zombie-like machine behavior toward robots capable of genuine understanding and self-awareness. His early research laid important groundwork in habit learning and neuro-inspired cognitive architectures, exploring how robots can intelligently balance deliberative, goal-directed decision-making with efficient habitual behaviors — an insight drawn from neuroscience and psychology. This thread of biologically inspired robotics also shaped his comparative investigations into model-based and model-free reinforcement learning, contributing practical frameworks for building more autonomous and adaptable robotic systems. More recently, Renaudo has expanded into applied computer vision and recycling automation, developing deep learning solutions for e-waste disassembly tasks. His 2019 systematic taxonomy of action representations further demonstrates his commitment to conceptual clarity within robotics research. Across more than a decade of contributions, Renaudo's work reflects a rare combination of theoretical ambition and real-world applicability, making him a distinctive voice in the pursuit of truly intelligent, self-aware machines.