Papers
9
Total Citations
338
H-Index
7
About
M. Ehrenmann is a pioneering researcher in human-robot interaction, specializing in intuitive robot programming and multimodal control. Their work centers on enabling non-expert users to command and teach robots through natural modalities, particularly gesture and speech. Ehrenmann’s most influential contribution is the development of programming by demonstration (PbD), a paradigm that allows robots to learn complex tasks by observing human demonstrations, eliminating the need for traditional coding. Their seminal paper, "Using gesture and speech control for commanding a robot assistant" (2003, 121 citations), established novel approaches for integrating verbal and gesture commands to make robot control more accessible. Another highly cited work, "Learning Robot Behaviour and Skills Based on Human Demonstration and Advice" (2000, 119 citations), advanced the machine learning framework for PbD. Ehrenmann also made key contributions to sensor fusion, designing multisensor systems that track user actions—combining visual and finger-measuring sensors—to improve observation accuracy in real-world environments. Their research, including the MEPHISTO path planning system, has been instrumental in bringing service robots into household settings, demonstrating that complex tasks can be mapped to robots through intuitive, human-like teaching methods.
Research Focus
Key Achievements
Top Papers
- 1Using gesture and speech control for commanding a robot assistant121 citations · 2003
- 2
- 3Programming service tasks in household environments by human demonstration45 citations · 2003
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- 5A sensor fusion approach for PbD12 citations · 2002
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- 7MEPHISTO A Modular and Extensible Path Planning System Using Observation8 citations · 1999
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- 9Erkennung dynamischer Gesten zur Kommandierung mobiler Roboter2 citations · 2000