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About

Hanna Ziesche is a robotics researcher whose work sits at the intersection of robot manipulation, machine learning, and autonomous grasping. Her research addresses some of the most pressing challenges in enabling robots to operate effectively in unstructured, real-world environments — from industrial bin-picking scenarios to flexible manufacturing assembly tasks. Ziesche's most influential contributions focus on advancing robotic grasping through innovative learning frameworks. Her hybrid approach combining motion primitives with learning-based methods (13 citations) and her model-free multi-suction cup grasping work (12 citations) have helped push the boundaries of gripper-agnostic manipulation systems. Her research on e-Bike motor assembly (9 citations) demonstrates a practical commitment to translating these techniques into advanced manufacturing contexts. Beyond grasping, she has contributed to episodic reinforcement learning through movement primitives, exploring black-box optimization strategies for robot control. Her more recent work on uncertainty-driven exploration for online grasp learning reflects a growing focus on adaptive systems capable of handling unseen objects and out-of-distribution scenarios. Across her published portfolio, Ziesche has accumulated over 40 citations, signaling meaningful early-career impact. Her research is particularly valuable for students and engineers working on industrial automation, where robust, generalizable manipulation remains an open and critical challenge.