Hanns Tappeiner

Papers

Total Citations

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About

Hanns Tappeiner is a robotics researcher whose work bridges machine learning and physical human-robot interaction. His key research areas include policy transfer in robotics, haptic feedback systems, and dynamic locomotion. Tappeiner’s most notable contribution is his pioneering work on transfer learning for trajectory libraries, where he demonstrated that policies learned for one robotic task can be adapted to new tasks without building libraries from scratch. His 2007 paper on this topic, with 38 citations, remains a foundational reference for researchers working on sample-efficient robot learning. In a more applied vein, Tappeiner explored remote haptic feedback for dynamic running machines, using the robot’s own legs as tactile sensors to give operators real-time ground feel—a creative approach to teleoperation challenges. Though his citation counts are modest, his work reflects a thoughtful integration of algorithmic transfer and embodied feedback, offering practical insights for students interested in making robots both smarter and more intuitive to control.