学習可能な安全度尺度
A Learnable Safety Measure
システム力学と失敗状態の関係を暗黙的に捉える安全度尺度を提案し、ガウス過程による能動サンプリングでモデルフリーに学習する手法を示した。学習中も推定尺度により失敗を大幅に削減できる。
著者: Steve Heim, Alexander von Rohr, Sebastian Trimpe, Alexander Badri-Spröwitz
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
Failures are challenging for learning to control physical systems since they risk damage, time-consuming resets, and often provide little gradient information. Adding safety constraints to exploration typically requires a lot of prior knowledge and domain expertise. We present a safety measure which implicitly captures how the system dynamics relate to a set of failure states. Not only can this measure be used as a safety function, but also to directly compute the set of safe state-action pairs. Further, we show a model-free approach to learn this measure by active sampling using Gaussian processes. While safety can only be guaranteed after learning the safety measure, we show that failures can already be greatly reduced by using the estimated measure during learning.
