AI-MOLE

Autonomous Iterative Motion Learning for unknown nonlinear dynamics with extensive experimental validation

verfasst von: Michael Meindl, Simon Bachhuber, Thomas Seel
Abstract: This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method that enables systems with unknown, nonlinear dynamics to autonomously learn to solve reference tracking tasks. The method iteratively applies an input trajectory to the unknown dynamics, trains a Gaussian process model based on the experimental data, and utilizes the model to update the input trajectory until desired tracking performance is achieved. Unlike existing approaches, the proposed method determines necessary parameters automatically, i.e., AI-MOLE works plug-and-play and without manual parameter tuning. Furthermore, AI-MOLE only requires input/output information, but can also exploit available state information to accelerate learning. While other approaches are typically only validated in simulation or on a single real-world testbed using manually tuned parameters, we present the unprecedented result of validating the proposed method on three different real-world robots and a total of nine different reference tracking tasks without requiring any a priori model information or manual parameter tuning. Over all systems and tasks, AI-MOLE rapidly learns to track the references without requiring any manual parameter tuning at all, even if only input/output information is available.
Organisationseinheit(en): Institut für Mechatronische Systeme
Externe Organisation(en): Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU Erlangen-Nürnberg)
Typ: Artikel
Journal: Control engineering practice
Band: 145
Anzahl der Seiten: 8
ISSN: 0967-0661
Publikationsdatum: 04.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Steuerungs- und Systemtechnik, Angewandte Informatik, Elektrotechnik und Elektronik, Angewandte Mathematik
Elektronische Version(en): https://doi.org/10.1016/j.conengprac.2024.105879 (Zugang: Geschlossen)