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About

Pauline Pounds is a robotics researcher whose work spans aerial robotics, bio-inspired sensing, and machine learning for real-world robotic systems. She is perhaps best known for her foundational contributions to quadrotor dynamics, with her 2010 paper "Modelling and Control of a Large Quadrotor Robot" accumulating over 514 citations and becoming a cornerstone reference in the unmanned aerial vehicle community. This work helped establish the theoretical and practical frameworks that continue to underpin modern drone development. Beyond aerial platforms, Pounds has pioneered bio-inspired tactile sensing through the development of lightweight whisker sensors that mimic mammalian vibrissae, enabling drones and robots to detect contact, proximity, and fluid velocity with remarkable sensitivity. Her research extends into multidimensional texture classification using whisker arrays, contributing novel datasets and perception algorithms to the field. Pounds has also explored the intersection of robotics and behavioral science through the PiRat framework, a creative system enabling controlled rat-robot social interaction studies. Her more recent work addresses the practical challenges of deploying deep reinforcement learning on physical robots, tackling the sim-to-real gap through asynchronous training architectures. Across these diverse threads, Pounds consistently bridges theoretical rigor with real-world applicability, making her a distinctive and innovative voice in modern robotics research.