Dave Stone

Papers

Total Citations

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About

Dave Stone’s research lies at the intersection of human-robot interaction and deep learning, with a focus on enabling intuitive, vision-based control of robotic systems. His most cited work, “Gesture Recognition for Robotic Control Using Deep Learning” (2017, 7 citations), tackles the challenge of using convolutional neural networks (CNNs) to recognize a small set of vehicle control gestures—such as “move forward,” “turn left,” and “stop”—directly from camera input. This study demonstrates how deep learning can bridge the gap between natural human motion and robotic commands, offering a practical, hands-free interface for operators. While modest in citation count, the work is notable for its early application of CNNs to real-time gesture-based robotic control, paving the way for more accessible and responsive human-robot collaboration. Stone’s contributions are particularly valuable for students and researchers exploring low-latency, vision-driven control systems, and his findings continue to inform developments in assistive robotics and autonomous vehicle interfaces.