Karime Pereida
Dynamic Systems Analysis (Canada), Vector Institute, University of Toronto
Papers
9
Total Citations
169
H-Index
7
About
Karime Pereida is a robotics and control systems researcher whose work sits at the intersection of adaptive control, machine learning, and autonomous systems. Her research focuses on enabling robots to perform with high precision in dynamic, uncertain, and previously unknown environments — a challenge central to deploying robots in real-world applications. Pereida's most influential contribution, her 2018 work on Adaptive Model Predictive Control for high-accuracy trajectory tracking (49 citations), addresses how robots can maintain performance despite disturbances, unmodeled dynamics, and parametric uncertainties. She has also made significant advances in transfer learning for robotics, developing data-efficient frameworks that allow systems to learn complex tasks from minimal demonstrations across multiple robots and scenarios. Her 2020 paper on high-speed mobile manipulator control (32 citations) pushed the boundaries of what collaborative robots can achieve dynamically, demonstrating accurate ball-catching behaviors. More recently, Pereida has explored bridging the gap between simulated models and real-world conditions using Lipschitz network adaptation. With over 160 cumulative citations, her body of work offers practical, theoretically grounded solutions for robust adaptive control, making her a notable voice in modern autonomous systems research.
Research Focus
Key Achievements
Top Papers
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- 6Bridging the Model-Reality Gap With Lipschitz Network Adaptation12 citations · 2021
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