Zhaole Sun

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About

Zhaole Sun is a robotics researcher whose work focuses on enabling robots to handle objects in challenging, ungraspable configurations—a key challenge in autonomous manipulation. His most-cited paper, "Learning Pregrasp Manipulation of Objects from Ungraspable Poses" (2020), has garnered 28 citations and addresses a fundamental limitation in robotic grasping: objects often lie in poses (e.g., flat boxes on a table) where no feasible grasp exists. Inspired by human bimanual manipulation, Sun’s approach teaches robots to first perform a pregrasp action—such as lifting or tilting—to transform the object into a graspable state before executing the final grasp. This work bridges the gap between perception and dexterous action, significantly expanding the range of objects robots can handle in unstructured environments. By learning these sequential manipulation skills from demonstration, Sun contributes to more adaptive and human-like robotic systems. His research has implications for warehouse automation, assistive robotics, and household tasks, where objects are rarely placed in ideal grasping orientations. With growing interest in dexterous manipulation, Sun’s work is poised to influence future learning-based approaches in robotic interaction.