TY - JOUR

T1 - AI-MOLE

T2 - Autonomous Iterative Motion Learning for unknown nonlinear dynamics with extensive experimental validation

AU - Meindl, Michael

AU - Bachhuber, Simon

AU - Seel, Thomas

PY - 2024/4

Y1 - 2024/4

N2 - 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.

AB - 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.

KW - Autonomous systems

KW - Iterative learning control

KW - Nonlinear systems

KW - Reference tracking

KW - Reinforcement learning

KW - Robot learning

UR - http://www.scopus.com/inward/record.url?scp=85184837982&partnerID=8YFLogxK

U2 - 10.1016/j.conengprac.2024.105879

DO - 10.1016/j.conengprac.2024.105879

M3 - Article

AN - SCOPUS:85184837982

VL - 145

JO - Control engineering practice

JF - Control engineering practice

SN - 0967-0661

M1 - 105879

ER -