Christopher Collander

Papers

Total Citations

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About

Christopher Collander’s research lies at the intersection of intelligent robotic rehabilitation, cognitive assessment, and 3D perception. His most cited work, “Magni Dynamics” (2017, 10 citations), introduces a vision-based kinematic and dynamic upper-limb model for home-based robotic rehabilitation. This system integrates adaptive haptic feedback control to deliver personalized therapy by adjusting resistance and support based on patient performance—a significant step toward accessible, intelligent rehabilitation. Collander also bridges robotics and cognitive science with a robot-based cognitive assessment model (2018, 2 citations) that evaluates visual working memory and attention, demonstrating how robots can serve as diagnostic tools. More recently, his 2021 work (2 citations) tackles a fundamental challenge in 3D sensing: learning the next best view for point clouds. By using reinforcement learning with a novel topology-based information gain metric, his approach prioritizes high-detail features like holes and concave surfaces, improving sensor efficiency in noisy environments. Though early in his career, Collander’s contributions show a clear trajectory toward human-centered robotics—combining rehabilitation, cognition, and perception to create systems that adapt to and understand human needs.