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About

Ioanna Mitsioni is a roboticist whose research lies at the intersection of data-driven control, contact-rich manipulation, and textile perception. Her work addresses fundamental challenges in robotic manipulation of deformable and complex objects—tasks where classical analytical models fall short. Mitsioni’s most significant contribution is in developing Data-Driven Model Predictive Control (DD-MPC) frameworks for contact-rich tasks such as food cutting, where she demonstrates how learned dynamics models can overcome the limitations of both purely analytical and purely learning-based approaches. Her 2019 paper on this topic has garnered 24 citations, establishing a foundation for safe, real-time control in unstructured environments. She has further advanced the field by addressing safety and performance guarantees for systems with temporally or spatially varying dynamics, as seen in her 2023 work. Beyond manipulation, Mitsioni has pioneered methods for textile taxonomy and classification using pulling and twisting motions, a critical step toward enabling robots to handle clothing for applications like assisted dressing, laundry folding, and textile recycling. Her work bridges the gap between theoretical control methods and practical robotic systems, making her a key figure in the push toward robots that can safely and reliably interact with the physical world.