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About

David Schiebener is a robotics researcher whose work sits at the intersection of computer vision, autonomous learning, and human-robot interaction. His research has consistently addressed one of the central challenges in robotics: enabling humanoid robots to perceive, understand, and manipulate objects in unstructured, real-world environments without prior knowledge of those objects. Schiebener's most influential contributions involve autonomous object representation and segmentation. His early work on visual multi-view object acquisition (42 citations) and interactive segmentation through physical manipulation (28–36 citations) demonstrated how robots could actively explore their environments to build rich object models from scratch. Rather than relying on predefined object databases, his systems allow robots to discover and learn about novel objects through embodied interaction — a significant conceptual leap for the field. His later research expanded into shape completion using symmetry cues (31 citations), haptic surface estimation (28 citations), and workspace analysis for human-robot collaboration (39 citations), reflecting a maturing research agenda focused on practical deployment alongside humans. His work on transferring grasping skills across different robotic platforms further underscores his commitment to generalizable solutions. With a portfolio accumulating over 260 citations, Schiebener has made meaningful contributions to autonomous robot perception and manipulation, providing tools and frameworks that continue to inform modern humanoid robotics research.