Michael Maravgakis
Papers
4
Total Citations
36
H-Index
3
About
Michael Maravgakis is a roboticist whose research focuses on the critical challenge of enabling legged robots to navigate reliably in unstructured, slippery, and unpredictable environments. His core contributions lie in contact state estimation—the difficult problem of determining whether a robot’s foot is stably planted, slipping, or losing contact with the ground. Maravgakis has pioneered the use of inertial information and deep learning to achieve robust contact detection, moving beyond traditional force-based methods. His 2023 paper on probabilistic contact state estimation using inertial data (21 citations) and his 2022 work on robust estimation in humanoid gaits (9 citations) are foundational to this area. He has also developed adaptive trajectory controllers for quadruped robots on slippery terrains, addressing stability during dynamic contact events. Beyond locomotion, Maravgakis explores the intersection of robotics and virtual reality, as seen in his 2025 work on a VR digital twin for surgical skill development. His research is directly applicable to search-and-rescue, industrial inspection, and humanoid robotics, where reliable locomotion is essential.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robust Contact State Estimation in Humanoid Walking Gaits9 citations · 2022
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