About

Martin Buss is a pioneering robotics researcher whose work spans human-robot interaction, rehabilitation robotics, and autonomous systems. Based at the Technical University of Munich, Buss has made transformative contributions to how robots perceive, collaborate with, and assist humans in unstructured environments. His landmark 2008 survey on human-robot collaboration (479 citations) helped define the intellectual foundation of this rapidly growing field, articulating the cognitive and physical requirements for robots operating alongside people. Complementing this, his work on compliant actuation for rehabilitation robots (448 citations) advanced the design of assistive devices such as LOPES, addressing critical challenges in safety and haptic rendering for patients undergoing gait therapy. Buss has also shaped the field of robotic perception, with his comparative analysis of surface normal estimation methods (296 citations) becoming a standard reference in 3D object recognition. His early work on dextrous grasping force optimization (288 citations) demonstrated elegant mathematical solutions for robotic manipulation. Further contributions include affective computing through gait analysis, gesture-based robot instruction using depth sensors, and realistic haptic handshaking via hidden Markov models — reflecting a remarkably broad yet cohesive research vision centered on intelligent, human-aware robotics.

Research Focus

Key Achievements

37
H-Index
199
Papers
6,197
Total Citations
31
Avg Citations/Paper
🏆 Most Cited Paper
HUMAN–ROBOT COLLABORATION: A SURVEY
479 citations · 2008
📈 Most Prolific Year: 2009 (27 Papers)
🤝 Key Collaborators: 226
🏛 Institutions: Technical University of Munich, Tokyo University of Science, Technische Universität Berlin, Institute for Advanced Study, Technische Universität Darmstadt, Institute of Automation

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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