About

Michael Gienger is a robotics researcher whose work spans humanoid locomotion, whole-body motion control, robot learning, and manipulation — contributing foundational advances across multiple decades of the field. He first gained significant recognition through his central role in developing "Johnnie," a dynamically stable biped robot, with a cluster of highly cited papers from 2002–2004 (accumulating over 490 citations combined) detailing the machine's mechanical design, sensor systems, and real-time control architecture. This work helped establish key methodologies for stable bipedal walking and jogging. Gienger subsequently extended his contributions into whole-body humanoid motion control and real-time collision avoidance, addressing the complex redundancy challenges inherent in multi-degree-of-freedom systems. His 2010 work on goal babbling for inverse kinematics learning (170 citations) introduced an elegant, bootstrapping approach to acquiring robot skills without expert knowledge. More recently, Gienger has shaped the growing fields of deformable object manipulation (244 citations) and simulation-to-real transfer in robot learning (101 citations), two of robotics' most pressing frontiers. With over 1,400 citations across his top works, his research consistently bridges theoretical rigor with practical robot deployment across industrial and service applications.

Research Focus

Key Achievements

33
H-Index
99
Papers
3,393
Total Citations
34
Avg Citations/Paper
🏆 Most Cited Paper
Challenges and Outlook in Robotic Manipulation of Deformable Objects
244 citations · 2022
📈 Most Prolific Year: 2010 (11 Papers)
🤝 Key Collaborators: 116
🏛 Institutions: Honda (Germany), Ludwig-Maximilians-Universität München, Technical University of Munich, Honda (Japan), Sustainable Europe Research Institute, Bielefeld University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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