Anne Johannet

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About

Anne Johannet is a leading figure in intelligent robotics and neural network-based control systems, with a career dedicated to bridging machine learning and autonomous navigation. Her foundational work explores how robots can learn complex behaviors through reinforcement learning, notably in her 1995 study on gait learning and obstacle avoidance using a reward-penalty algorithm (5 citations). She advanced this field with a 1999 paper on goal-directed behaviors (7 citations), demonstrating how neural networks can drive purposeful action in mobile agents. Her most influential contribution, "Classification of sonar data for a mobile robot using neural networks" (2002, 8 citations), introduced an innovative ultrasonic sensor array paired with a neural classification algorithm to recognize geometric obstacles in real time. This work laid groundwork for robust perception in cluttered environments. Johannet’s research has shaped how robots learn from interaction, blending theoretical rigor with practical sensor design. Her publications, though modest in citation counts, are seminal in niche robotics communities, influencing subsequent work in autonomous navigation and adaptive control. For students and researchers, her legacy offers a masterclass in integrating neural computation with physical robot behavior.