Max Robotics — Global Service Robot Directory + US Certification

About

Armin Steinhauser is a robotics and control systems researcher whose work centers on motion optimization, iterative learning control, and robot calibration for industrial manipulators. His most significant contribution lies in developing data-driven, iterative approaches to time-optimal path tracking — methods that address the fundamental challenge of model-plant mismatch, where discrepancies between a robot's theoretical model and its real-world behavior degrade tracking performance. His 2018 paper on iterative learning for time-optimal path tracking has garnered 53 citations, establishing him as a notable voice in this specialized field. Steinhauser has also made meaningful advances in robot calibration, proposing a two-stage method that explicitly accounts for joint and drive flexibilities — factors often neglected in standard calibration procedures — thereby improving positioning accuracy in practical settings. His 2016 work on a fast pick-and-place prototype robot demonstrates a broader systems-level competency, integrating vision, mechanical design, and control into a functional research platform. Across his body of work, Steinhauser consistently bridges theoretical optimization frameworks with real hardware implementation, making his research particularly valuable for engineers and researchers seeking deployable solutions in advanced industrial robotics.