Markus Leitner

Papers

Total Citations

H-Index

About

Markus Leitner is a roboticist whose research centers on enabling robots to perceive and manipulate objects in complex, real-world environments. His primary focus lies at the intersection of computer vision and robotic manipulation, particularly in the challenging domains of object learning and grasping. Leitner’s major contribution is a pioneering pipeline that allows robots to autonomously generate high-quality 3D object models from RGB-D videos captured during in-hand manipulation. This work, published in 2021 with 10 citations, directly addresses the critical bottleneck of model availability in unstructured settings like homes, enabling robust 6D pose estimation and subsequent grasping. He further pushes the boundaries of perception by tackling the notoriously difficult problem of grasping transparent objects, as detailed in his 2022 paper "Grasping the Inconspicuous." By developing methods to overcome the noisy and distorted data produced by standard 3D sensors on glass-like surfaces, Leitner is expanding the practical applicability of robotic systems. His work is vital for advancing domestic robotics, where encountering novel and visually challenging objects is the norm, not the exception.