Katrine Seel
Papers
2
Total Citations
37
H-Index
2
About
Katrine Seel is a researcher at the intersection of robotics, control theory, and safe autonomous systems. Her work primarily focuses on bridging the gap between model-based control and learning-based methods, with a strong emphasis on safety guarantees for real-world applications. Seel’s most cited paper, "Safe Learning for Control using Control Lyapunov Functions and Control Barrier Functions: A Review" (2021, 33 citations), provides a comprehensive synthesis of how these functions can ensure stability and safety in systems where accurate models are unavailable—a critical challenge for autonomous robots. This review has become a key reference for researchers working on safe reinforcement learning and control. In her earlier work, "Robotic Bin-Picking under Geometric End-Effector Constraints" (2019, 4 citations), Seel tackled a practical industrial problem: optimizing bin placement and grasp selection to ensure path reachability. By integrating workspace mapping with grasp planning, she demonstrated how to account for robot-specific constraints in bin-picking tasks. Her contributions highlight a commitment to both theoretical rigor and practical deployability, making her a rising voice in safe, learning-enabled robotics.
Research Focus
Key Achievements
Top Papers
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