Simon Bachhuber

Simon Bachhuber

ML researcher for robotics

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Recurrent Inertial Graph-Based Estimator (RING): A Single Pluripotent Inertial Motion Tracking Solution

Published in Transactions on Machine Learning Research, 2024

This paper presents RING, a novel machine learning-based, plug-and-play method for Inertial Motion Tracking (IMT) that eliminates the need for expert knowledge by employing a decentralized network of recurrent neural networks, enabling broad applicability, zero-shot generalization from simulation to experimental data, and high performance across diverse IMT challenges, including magnetometer-free and sparse sensing setups.

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Conference Papers

A Soft Robotic System Automatically Learns Precise Agile Motions Without Model Information

Published in 2024 IEEE/RSJ international conference on intelligent robots and systems, 2024

This work demonstrates that the Automatic Neural ODE Control (ANODEC) method enables practical and efficient control of pneumatic soft robots (SRs) with hysteresis effects, achieving agile, non-repetitive reference tracking from only 30 seconds of input-output data and outperforming manually tuned PID controllers, thereby advancing the feasibility of data-driven, low-expertise SR control.

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RNN-based observability analysis for magnetometer-free sparse inertial motion tracking

Published in 2022 25th International Conference on Information Fusion, 2022

We present an RNN-based method for assessing the observability of relative pose in sparse, magnetometer-free inertial motion tracking (IMT) systems, demonstrating that observability and tracking accuracy depend on the kinematic structure, thus advancing reliable and cost-effective IMT solutions for complex kinematic chains.

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