Anne van der Horst
Papers
1
Total Citations
4
H-Index
1
About
Anne van der Horst is a rising researcher at the intersection of human-robot interaction and optimal control, with a primary focus on the automatic tuning of shared control systems. Her most-cited work introduces a Bayesian optimization framework for the automatic tuning of Model Predictive Control (MPC)-based shared controllers, a critical advancement for applications where humans and robots collaborate in dynamic environments. By designing novel performance metrics and user input representations for simulation-based optimization, van der Horst addresses the long-standing challenge of manually calibrating complex control parameters. This framework enables more intuitive, efficient, and safer human-robot collaboration, particularly in assistive and autonomous driving contexts. Though early in her career, her 2024 paper has already garnered 4 citations, signaling growing interest from the control and robotics communities. Her contributions are notable for bridging the gap between theoretical control optimization and practical, user-centered design—a key step toward making shared autonomy systems more accessible and robust. As she continues to develop her research, van der Horst is poised to become a leading voice in data-driven, human-aware control systems.
Research Focus
Key Achievements
Top Papers
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