Saray Bakker

Eindhoven University of Technology, Delft University of Technology

Papers

Total Citations

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About

Saray Bakker is a rising researcher at the forefront of intelligent robotic systems, with a focus on human-robot interaction and multi-robot coordination. Her work bridges the gap between theoretical optimization and practical deployment, particularly in shared control and real-time motion planning. In her 2024 paper, she introduced a Bayesian optimization framework for the automatic tuning of Model Predictive Control (MPC)-based shared controllers, enabling robots to adapt to human operators more efficiently—a critical step toward intuitive human-robot collaboration. Her 2023 contribution, "Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics," extends dynamic fabrics to multi-robot systems, creating the Multi-Robot Dynamic Fabrics (MRDF) method. This geometric approach allows multiple robotic manipulators to plan motions in real-time while operating in close proximity, addressing a key challenge in industrial and warehouse automation. Though early in her career, Bakker’s work has already garnered citations from the robotics community, signaling its growing influence. Her research is particularly notable for its emphasis on simulation-based optimization and practical deployment, making her a promising voice in the next generation of autonomous systems.