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About

Wout Boerdijk is a researcher specializing in computer vision and perception for autonomous robotic systems, with particular focus on object segmentation, terrain analysis, and robotic manipulation in challenging environments. His work spans two compelling frontiers: planetary exploration robotics and assistive robotics, bridging cutting-edge machine learning with real-world autonomous applications. Among his most notable contributions is his research on uncertainty estimation for planetary terrain segmentation (2023, 9 citations), addressing the fundamental challenge of ambiguous training data in space robotics contexts. Complementing this, his work on autonomous rock instance segmentation demonstrates a commitment to enabling reliable robotic decision-making during extra-terrestrial missions. On the terrestrial side, Boerdijk has advanced the field of unknown object perception — a notoriously difficult problem — through both segmentation from stereo imagery (6 citations) and practical grasping pipelines for assistive robotics applications. His earlier contributions include self-supervised segmentation techniques that reduce dependency on costly manual annotation, and a flexible multi-body tracking framework capable of handling complex kinematic structures. Collectively, Boerdijk's research pushes autonomous robots toward greater adaptability in unstructured, real-world environments, making him a noteworthy emerging voice in robot perception and space robotics.