About

Michael Bloesch is a prominent robotics researcher whose work sits at the intersection of legged robotics, state estimation, and autonomous navigation. Best known for his foundational contributions to the ANYmal quadrupedal robot platform — which has accumulated over 1,400 citations across multiple publications — Bloesch helped pioneer the development of compliant, torque-controllable robotic systems capable of robust dynamic locomotion in challenging real-world environments. His 2016 ANYmal paper alone has garnered 864 citations, establishing it as a landmark reference in legged robotics. Beyond hardware, Bloesch has made significant contributions to the algorithmic side of mobile robotics. His 2013 work on state estimation for legged robots introduced an Observability Constrained Extended Kalman Filter that elegantly fuses leg kinematics with IMU data, earning over 246 citations. He has also advanced probabilistic terrain mapping under uncertain localization conditions and contributed to dynamic SLAM systems, including the innovative MID-Fusion approach for object-level scene understanding. Earlier work on the StarlETH quadruped further demonstrates his long-standing commitment to versatile, efficient locomotion. Across these domains, Bloesch's research has profoundly shaped how robots perceive, map, and move through unstructured environments.

Research Focus

Key Achievements

25
H-Index
55
Papers
4,489
Total Citations
82
Avg Citations/Paper
🏆 Most Cited Paper
ANYmal - a highly mobile and dynamic quadrupedal robot
864 citations · 2016
📈 Most Prolific Year: 2013 (10 Papers)
🤝 Key Collaborators: 107
🏛 Institutions: ETH Zurich, Imperial College London, Institute of Robotics, Google DeepMind (United Kingdom), École Polytechnique Fédérale de Lausanne, Google (United States)

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago