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About

Hannes Kisner is a researcher advancing the intersection of robotics, sensing, and machine learning. His primary research areas include capacitive proximity sensing, material detection, and human-robot interaction. Kisner’s key contribution lies in applying machine learning to capacitive sensors, enabling robots to detect and classify materials without physical contact—a critical capability for adaptive grasping and locomotion. His most-cited work, “Using Machine Learning for Material Detection with Capacitive Proximity Sensors” (2020, 13 citations), demonstrates how impedance data can be used to identify surfaces, allowing robots to adjust their behavior accordingly. He further refined this approach in his 2022 study on capacitive material detection for robotic grasping. Beyond sensing, Kisner has contributed to calibration techniques, developing a method for hand-eye and camera-to-camera calibration in systems with limited fields of view (2017, 3 citations). His recent work on 3D hand and object pose estimation for real-time human-robot interaction (2022) highlights his commitment to creating intuitive, responsive robotic systems. With a focus on practical, sensor-driven solutions, Kisner’s research is paving the way for more intelligent and adaptable robots.