Papers
20
Total Citations
275
H-Index
10
About
Oliver Urbann is a robotics researcher whose work spans bipedal locomotion control, sensor fusion, and reinforcement learning for autonomous systems. He is best known for his pioneering contributions to dynamic walking control in biped robots, most notably his 2009 paper "Observer-based dynamic walking control for biped robots," which has accumulated 74 citations and established foundational techniques for stable, real-time bipedal motion. Building on this work, Urbann extended these principles to incorporate three-dimensional upper body motion and human-like walking patterns, demonstrating a sophisticated understanding of biomechanical dynamics translated into robotic systems. A recurring theme across his research is the integration of observer-based methods and sensor fusion — his work on Unscented Kalman Filtering for robust robot localization reflects a commitment to reliable, real-world performance. More recently, Urbann has expanded into deep reinforcement learning, contributing a well-regarded survey on guided RL for robotics (2022) and applying these methods to multi-robot navigation and novel platforms like the evoBOT, a two-wheeled compound inverted pendulum robot. With over 200 cumulative citations, his career traces a coherent arc from classical control theory toward modern learning-based approaches, positioning him as a versatile and impactful contributor to autonomous robotics research.
Research Focus
Key Achievements
Top Papers
- 1Observer-based dynamic walking control for biped robots74 citations · 2009
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- 5Observer based biped walking control, a sensor fusion approach17 citations · 2013
- 6Applying Dynamic Walking Control for Biped Robots15 citations · 2010
- 7Flexible Linear Inverted Pendulum Model for cost-effective biped robots14 citations · 2015
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