Andreas Jordt

Papers

Total Citations

102

H-Index

About

Andreas Jordt is a leading researcher in robotic perception and manipulation, with a focus on the challenging domain of deformable objects. His work bridges computer vision and robotics to enable systems to handle materials that change shape during interaction. Jordt’s most cited paper, “An Adaptable Robot Vision System Performing Manipulation Actions With Flexible Objects” (2014, 70 citations), introduces a pioneering framework for executing complex operations like peg-in-hole and laying-down actions on flexible materials, overcoming traditional path-planning limitations. He further advanced this field with “Simultaneous Estimation of Material Properties and Pose for Deformable Objects from Depth and Color Images” (2012, 20 citations), enabling robots to infer both physical properties and spatial configuration of non-rigid objects. Earlier, Jordt contributed foundational work in “Automatic high-precision self-calibration of camera-robot systems” (2009, 12 citations), developing a method for simultaneous, numerically stable calibration of intrinsic and extrinsic camera parameters using a single point marker. His cumulative impact—evidenced by over 100 citations—demonstrates his role in making robotic systems more adaptable and precise, particularly for handling the unpredictable nature of flexible objects.