RNN-based Observability Analysis for Magnetometer-Free Sparse Inertial Motion Tracking. - Simon Bachhuber; Daniel Weber 005; Ive Weygers; Thomas Seel (FUSION, 2022)

VQF: A Milestone in Accuracy and Versatility of 6D and 9D Inertial Orientation Estimation. - Daniel Laidig; Ive Weygers; Simon Bachhuber; Thomas Seel (FUSION, 2022)

Neural ODEs for Data-Driven Automatic Self-Design of Finite-Time Output Feedback Control for Unknown Nonlinear Dynamics. - Simon Bachhuber; Ive Weygers; Thomas Seel (IEEE Control. Syst. Lett., 2023)

Reference-Adapting Iterative Learning Control for Motion Optimization in Constrained Environments. - Michael Meindl; Simon Bachhuber; Thomas Seel (CDC, 2024)

A Soft Robotic System Automatically Learns Precise Agile Motions Without Model Information. - Simon Bachhuber; Alexander Pawluchin; Arka Pal; Ivo Boblan; Thomas Seel (IROS, 2024)

AI-MOLE: Autonomous Iterative Motion Learning for Unknown Nonlinear Dynamics with Extensive Experimental Validation. - Michael Meindl; Simon Bachhuber; Thomas Seel (CoRR, 2024)

Dispelling Four Challenges in Inertial Motion Tracking with One Recurrent Inertial Graph-based Estimator (RING). - Simon Bachhuber; Ive Weygers; Thomas Seel (CoRR, 2024)

Recurrent Inertial Graph-Based Estimator (RING): A Single Pluripotent Inertial Motion Tracking Solution. - Simon Bachhuber; Ive Weygers; Dustin Lehmann; Mischa Dombrowski; Thomas Seel (Trans. Mach. Learn. Res., 2024)