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About

Nina Zumel’s research spans robotics, artificial intelligence, and human-computer interaction, with a focus on enabling machines to perceive, manipulate, and coordinate in dynamic environments. Her early work on robotic perception of materials introduced shape-invariant acoustic measures for material type recognition (57 citations), pioneering non-visual sensing for object identification. She made significant contributions to design rationale capture, developing automated methods to document detailed design processes—a challenge long recognized but rarely solved due to time constraints (49 citations). In multi-robot systems, Zumel advanced team coordination through desirability functions, extending single-robot behavior strategies to collaborative teams (19 citations). Her work on nonprehensile manipulation, including parts orienting without grasping (12 citations) and balancing planar bouncing objects (18 citations), challenged quasi-static assumptions in manipulation tasks, addressing real-world scenarios where objects move unpredictably. Notable achievements include her 2002 paper on balancing bouncing objects, which influenced dynamic manipulation research, and her 2018 revisit of the topic, reflecting sustained interest. With over 150 total citations, Zumel’s research bridges perception, planning, and coordination, offering foundational insights for students and researchers in robotics and AI.