Zhanna Pershina

Papers

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About

Zhanna Pershina’s research lies at the intersection of robotics, computer vision, and intelligent manipulation systems, with a focus on enabling machines to perceive and interact with their environment as intuitively as humans do. Her major contributions include the development of a novel pneumatic-mechanical gripper for industrial bin picking—a task that, despite its apparent simplicity, has long challenged roboticists. This work, which has garnered 7 citations, addresses the fundamental problem of grasping unfamiliar objects safely and optimally, mimicking the human ability to select an appropriate grip on sight. In parallel, Pershina has advanced visual navigation for mobile robots by analyzing deep convolutional neural networks for object recognition. Her 2018 paper on this topic, with 5 citations, evaluates state-of-the-art algorithms and their applicability to real-world navigation challenges. Through these contributions, Pershina bridges the gap between perception and action in robotics, tackling core problems that have puzzled researchers for decades. Her work is particularly notable for its practical orientation, aiming to equip industrial manipulators and autonomous robots with the dexterity and visual intelligence needed to operate in unstructured environments.