Andy Gillies

Papers

Total Citations

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About

Andy Gillies is a researcher whose work sits at the intersection of human-robot interaction and applied deep learning. His primary focus is on enabling intuitive, vision-based control of robotic systems, most notably through gesture recognition. In his seminal 2017 paper, "Gesture Recognition for Robotic Control Using Deep Learning," Gillies explored whether convolutional neural networks (CNNs) could reliably interpret a small set of vehicle control gestures—such as "move forward," "turn left," and "stop"—directly from a camera feed. This work demonstrated a practical, low-latency pathway for non-experts to command robots without traditional interfaces, a contribution that has garnered 7 citations and laid groundwork for more natural human-robot collaboration. While his citation count reflects a focused, emerging impact, Gillies’ research is notable for its clarity of application: he directly addressed the feasibility of deploying CNNs in real-time control loops, a challenge that remains central to modern robotics. His achievements highlight a commitment to bridging computer vision and robotic actuation, making his profile a compelling read for students interested in the practical deployment of deep learning in autonomous systems.