GoSafe: 大域的最適な安全ロボット学習
GoSafe: Globally Optimal Safe Robot Learning
SafeOptの探索範囲を初期安全領域外に拡張し、学習したバックアップ制御器で安全を確率的に保証しながら大域最適解への収束条件を導出、実機実験で検証した。
著者: Dominik Baumann, Alonso Marco, Matteo Turchetta, Sebastian Trimpe
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
When learning policies for robotic systems from data, safety is a major concern, as violation of safety constraints may cause hardware damage. SafeOpt is an efficient Bayesian optimization (BO) algorithm that can learn policies while guaranteeing safety with high probability. However, its search space is limited to an initially given safe region. We extend this method by exploring outside the initial safe area while still guaranteeing safety with high probability. This is achieved by learning a set of initial conditions from which we can recover safely using a learned backup controller in case of a potential failure. We derive conditions for guaranteed convergence to the global optimum and validate GoSafe in hardware experiments.
