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About

Rita Laezza is a roboticist whose research focuses on one of the field’s most challenging frontiers: the manipulation of deformable linear objects (DLOs)—such as cables, wires, and ropes. Unlike rigid objects, DLOs are notoriously difficult to model, simulate, and control due to their infinite degrees of freedom and complex elastoplastic behaviors. Laezza has made foundational contributions to this domain, pioneering methods that combine planning, control, and learning to enable robots to reliably shape, route, and manipulate these flexible materials. Her most-cited work, “Learning Shape Control of Elastoplastic Deformable Linear Objects” (2021, 27 citations), introduces a framework for teaching robots to achieve precise shape configurations. She further advanced the state of the art with “Planning and Control for Cable-routing with Dual-arm Robot” (2022, 26 citations), which solves the practical problem of clipping DLOs into fixtures—a task critical for manufacturing and assembly. Laezza also developed ReForm (2021, 23 citations), an open-source robot learning sandbox that provides a standardized environment for benchmarking DLO manipulation algorithms. Her work is notable for bridging model-based planning with reinforcement learning, and for addressing real-world industrial challenges where deformable objects are ubiquitous but automation remains scarce.