Bas Meere
Papers
1
Total Citations
4
H-Index
1
About
Bas Meere is a researcher at the forefront of human-robot interaction and shared control systems, with a particular focus on optimizing Model Predictive Control (MPC) frameworks. His key research areas include Bayesian optimization, autonomous driving, and assistive robotics, where he seeks to bridge the gap between human intent and machine precision. Meere’s major contribution lies in developing a Bayesian optimization framework for the automatic tuning of MPC-based shared controllers, a breakthrough that enables more intuitive and efficient collaboration between humans and robots. His most-cited 2024 paper, which has already garnered 4 citations, introduces novel performance metrics and user input representations for simulation-based optimization, paving the way for safer and more responsive autonomous systems. This work holds significant promise for applications in advanced driver-assistance systems and rehabilitation robotics. Meere’s research is characterized by its practical impact, offering a data-driven approach to fine-tuning complex control systems without extensive manual calibration. His achievements reflect a deep commitment to making shared autonomy more accessible and reliable, positioning him as an emerging voice in the field of intelligent control and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1