AI-MOLE: 未知の非線形ダイナミクスに対する自律的反復運動学習と広範な実験検証
AI-MOLE: Autonomous Iterative Motion Learning for Unknown Nonlinear Dynamics with Extensive Experimental Validation
未知の非線形ダイナミクスを持つシステムが、入出力データからガウス過程モデルを反復的に学習し、手動パラメータ調整なしで参照追従タスクを自律的に獲得する手法を提案し、3種類の実機ロボットと9つのタスクで検証した。
著者: Michael Meindl, Simon Bachhuber, Thomas Seel
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
