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Anastasia Mavrommati is a robotics researcher whose work spans soft robotics, control theory, and autonomous systems. Her key contributions lie in developing novel robotic platforms and algorithms for motion planning, control, and localization in challenging environments. She is perhaps best known for designing and implementing a binary redundant manipulator—a snakelike robot powered by shape memory alloy (SMA) wires that act as binary actuators, a foundational paper with 18 citations. Mavrommati has also made significant advances in data-driven measurement models for active localization in sparse environments (11 citations), enabling robots to explore and build models for future tasks. Her work on trajectory planning for robotic fish using ergodic exploration (10 citations) addresses efficient underwater monitoring, while her research on optimal control-on-request for real-time assistive balance control (8 citations) demonstrates practical applications in human-robot interaction. More recently, she has applied model predictive control to reactive motion planning for robot manipulators (5 citations), enabling safe operation near moving obstacles. Mavrommati’s impact is further evidenced by her role in organizing the Robotic Grasping and Manipulation Competition at ICRA 2024, highlighting her leadership in the field. Her research, totaling over 60 citations, continues to push the boundaries of adaptive, efficient, and intelligent robotic systems.