Marcus Dominguez-Kuhne

University of Southern California, University of California, Berkeley

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Total Citations

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About

Marcus Dominguez-Kuhne is a roboticist whose research focuses on the intersection of manipulation, perception, and learning, with a particular emphasis on solving real-world challenges in unstructured environments. His work targets two critical, yet underexplored, areas: deformable object manipulation and mechanical search in constrained spaces. In his highly cited 2022 paper, "Learning Deformable Object Manipulation From Expert Demonstrations," Dominguez-Kuhne introduced DMfD, a novel Learning from Demonstration method that enables robots to handle complex, non-rigid materials like fabric or cables using either state or image inputs. This work, with 32 citations, provides a framework for balancing multiple demonstration strategies to achieve robust performance. His earlier contributions, including the 2021 paper on "Mechanical Search on Shelves using Lateral Access X-RAY" (21 citations), address the practical problem of locating occluded objects in lateral-access environments like shelves and cabinets. Here, he developed LAX-RAY, a system that efficiently reduces occupancy uncertainty to find hidden items. Dominguez-Kuhne’s research is notable for bridging the gap between theoretical robotics and practical applications in warehouses, retail, and healthcare, demonstrating significant impact through both citation counts and the immediate relevance of his solutions to industry and everyday settings.