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About

Ulrich Hillenbrand is a robotics researcher whose career has centered on dexterous robotic manipulation, humanoid arm systems, and robot learning — fields that sit at the intersection of mechanical design, computer vision, and artificial intelligence. His most influential contribution, the humanoid two-arm system built on DLR's Lightweight-Robot-III and DLR-Hand-II platforms, has garnered over 229 citations and established a foundational research infrastructure for studying complex two-handed manipulation with human-like dexterity. Complementing this hardware work, Hillenbrand advanced the field of imitation learning by developing methods for generalizing human grasping strategies to multi-fingered robotic hands, earning over 86 citations. His research spans the full manipulation pipeline — from low-level pose estimation using stereo clustering to high-level task and path planning for autonomous humanoid systems. Hillenbrand has also contributed to healthcare robotics, exploring heterogeneous robotic assistants for caregiving environments, reflecting a commitment to real-world societal impact. More recently, his work on shape completion with uncertainty prediction addresses a critical challenge in robotic perception. Together, his publications paint a portrait of a researcher dedicated to closing the gap between human dexterity and robotic capability.