M. Yunus Seker
Papers
1
Total Citations
2
H-Index
1
About
M. Yunus Seker is an emerging researcher working at the intersection of robotics, machine learning, and physical simulation. His work focuses on enabling robots to better understand and interact with the physical world by estimating the material and dynamic properties of objects through intelligent observation. His most recognized contribution, "Estimating Material Properties of Interacting Objects Using Sum-GP-UCB" (2024), introduces a Bayesian optimization framework that allows robotic systems to identify key material property parameters from real-world observations — a critical capability for accurate physical simulation and dexterous manipulation. By leveraging Gaussian Process-based upper confidence bound strategies, Seker's approach offers a principled, data-efficient solution to a longstanding challenge in robot learning and simulation. Though early in his citation trajectory with 2 citations to date, his research addresses a foundational problem in embodied AI and physically-grounded robotics, areas of rapidly growing importance. His work positions him as a promising contributor to the field of robot perception and adaptive simulation, with potential applications spanning industrial automation, manipulation, and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Estimating Material Properties of Interacting Objects Using Sum-GP-UCB2 citations · 2024