Matthew Nokleby

Wayne State University, Brigham Young University

Papers

Total Citations

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About

Matthew Nokleby is a researcher at the intersection of robotics, autonomous systems, and human-machine interaction, with a particular focus on surgical robotics and multi-agent coordination. His most cited work, "A Robotic Recording and Playback Platform for Training Surgeons and Learning Autonomous Behaviors Using the da Vinci Surgical System" (2019, 23 citations), introduces a novel platform that synchronously records stereo laparoscopic video, robot arm joint angles, and surgeon-console interactions. This system enables on-demand playback for surgical training and provides a rich dataset for developing autonomous behaviors, representing a significant step toward semi-autonomous robotic surgery. In earlier work, Nokleby explored "Satisficing Coordination and Social Welfare for Robotic Societies" (2008, 9 citations), where he addressed how groups of robots can achieve efficient coordination without requiring optimal solutions—a concept with implications for swarm robotics and distributed decision-making. His contributions bridge practical engineering for surgical robotics with theoretical frameworks for multi-agent systems, making his work valuable for both clinicians and roboticists. Nokleby’s research continues to influence how robots learn from human demonstration and collaborate in complex, real-world environments.