Reni Jordanowa
Papers
1
Total Citations
6
H-Index
1
About
Reni Jordanowa is a roboticist advancing the frontier of force-sensitive manipulation in uncertain environments. Her research centers on model predictive control (MPC) and machine learning integration, with a particular focus on enabling robots to handle physical interactions—such as applying precise forces or moments—when the environment is unpredictable or poorly modeled. Her most cited work, "Direct Force Feedback using Gaussian Process based Model Predictive Control" (2020, 6 citations), addresses a critical challenge in robotics: controlling contact forces while respecting system constraints, even when the robot’s surroundings are highly variable. By embedding Gaussian process regression into an MPC framework, Jordanowa’s approach allows robots to learn and adapt to unknown dynamics in real time, offering a robust solution for tasks like assembly, polishing, or surgical assistance. Though early in her career, her contributions are already cited by peers working on learning-based control and compliant manipulation. Jordanowa’s work stands out for its practical focus on closing the loop between perception, prediction, and physical interaction—a key step toward robots that can safely and dexterously operate alongside humans in unstructured settings.
Research Focus
Key Achievements
Top Papers
- 1