Michael Ketzner

Papers

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About

Michael Ketzner is a researcher in human-robot interaction, with a focus on developing accessible, engaging interfaces between humans and autonomous systems. His work centers on programming social robots for interactive gameplay, leveraging both commercial robotics platforms and computer vision techniques. In his most cited paper, "Teaching the NAO Robot to Play a Human-Robot Interactive Game" (2019, 5 citations), Ketzner details the integration of Choreographe programming with OpenPose pose detection to enable the NAO humanoid robot to play "Simon Says" with human players. This contribution demonstrates a practical framework for teaching robots to perceive and respond to human gestures in real time, advancing the field of socially assistive robotics. Ketzner’s research is notable for bridging low-level robot control with high-level interaction design, offering a replicable model for educational and therapeutic applications. His work underscores the potential of off-the-shelf robots to serve as intuitive partners in collaborative play, making him a valuable voice in the growing dialogue on human-robot teamwork.