Savva Morozov

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About

Savva Morozov is a roboticist advancing the frontier of contact-rich manipulation through convex optimization. His research centers on motion planning for robots that physically interact with their environment—tasks where making and breaking contact creates a hybrid, discrete-continuous challenge. In his landmark 2024 paper, "Towards Tight Convex Relaxations for Contact-Rich Manipulation," Morozov introduces a transformative approach that reformulates this hybrid problem as a shortest-path graph search, leveraging tight convex relaxations to guarantee global optimality. This work, already garnering 11 citations, directly addresses the computational intractability that has long plagued manipulation planning, offering a principled path toward reliable, real-time robot control. By bridging optimization theory and robotic dexterity, Morozov’s contributions promise to unlock more robust automation in assembly, grasping, and tool use. His research stands out for its mathematical rigor and practical vision, positioning him as a rising leader in the intersection of convex optimization and robotic manipulation.