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About

Madison Clark-Turner is a leading researcher in the intersection of robotics, artificial intelligence, and human-robot interaction (HRI), with a primary focus on learning from demonstration (LfD) and imitation learning. Her work addresses the critical challenge of enabling robots to acquire complex, high-level cognitive skills—such as abstract reasoning and temporal reasoning—directly from human demonstrations, rather than through explicit programming. Clark-Turner’s major contributions include pioneering the use of deep recurrent Q-networks (DRQNs) for autonomous behavioral intervention delivery, as demonstrated in her 2017 paper (13 citations), and developing robust behavior cloning methods that can detect and reject adversarial or incorrect demonstrations, a practical advancement for real-world deployment (2021, 5 citations). Her most impactful work, "Deep Reinforcement Learning of Abstract Reasoning from Demonstrations" (2018, 17 citations), introduces a framework for extracting generalizable rules governing complex human interactions, marking a significant milestone in developmental robotics. Across her publications, Clark-Turner has accumulated over 40 citations, and her research has been applied to social-skills training using NAO humanoid robots, showcasing her commitment to translating theoretical advances into tangible, socially beneficial technologies.