Nina Wolleb

Papers

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About

Nina Wolleb’s research sits at the intersection of human-robot interaction (HRI) and computer vision, with a focus on making robotic systems more intuitive and responsive to human cues. Her most influential work, “SEPO: Selecting by Pointing as an Intuitive Human-Robot Command Interface” (2013, 38 citations), pioneered the use of natural pointing gestures—captured via a Kinect sensor—as a command interface for robots. This contribution demonstrated how everyday human communication could be leveraged to control robotic actions, significantly advancing the field of gesture-based HRI. Building on this, Wolleb co-authored “Tracking Benchmark and Evaluation for Manipulation Tasks” (2015, 32 citations), which introduced a public dataset and evaluation framework for tracking algorithms in high-degree-of-freedom manipulation scenarios. This work provided a critical resource for benchmarking, enabling more accurate and reliable tracking in complex robotic tasks. By combining intuitive interface design with rigorous evaluation tools, Wolleb’s research has laid foundational groundwork for more natural and effective human-robot collaboration, earning her recognition as a key contributor to practical, user-centered robotics.