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.

翻译：本文提出自主迭代运动学习（AI-MOLE）方法，该方法使具有未知非线性动力学的系统能够自主学会求解参考跟踪任务。该方法通过迭代地将输入轨迹施加于未知动力学，基于实验数据训练高斯过程模型，并利用该模型更新输入轨迹，直至达到期望的跟踪性能。与现有方法不同，所提方法能自动确定必要参数，即AI-MOLE以即插即用方式工作，无需手动参数调优。此外，AI-MOLE仅需输入/输出信息，但也可利用可用的状态信息加速学习。现有方法通常仅在仿真或单一真实实验平台上使用手动调参验证，而本文展示了前所未有的成果：在三个不同真实机器人平台上，针对总共九种不同参考跟踪任务，无需任何先验模型信息或手动参数调优，即验证了所提方法的有效性。在所有系统与任务中，即使仅使用输入/输出信息，AI-MOLE也能快速学习跟踪参考轨迹，完全无需手动参数调优。