安全な探索を実現する学習ベースモデル予測制御
Learning-based Model Predictive Control for Safe Exploration
ガウス過程による不確実性推定と終端集合制約を組み合わせ、確率的な安全保証を備えた学習ベースMPCを提案し、動的システムの安全かつ効率的な探索を実現した。
著者: Torsten Koller, Felix Berkenkamp, Matteo Turchetta, Andreas Krause
分類: eess.SY, cs.AI, cs.LG, cs.RO, cs.SY
原文アブストラクト
Learning-based methods have been successful in solving complex control tasks without significant prior knowledge about the system. However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, real-world applications. In this paper, we present a learning-based model predictive control scheme that can provide provable high-probability safety guarantees. To this end, we exploit regularity assumptions on the dynamics in terms of a Gaussian process prior to construct provably accurate confidence intervals on predicted trajectories. Unlike previous approaches, we do not assume that model uncertainties are independent. Based on these predictions, we guarantee that trajectories satisfy safety constraints. Moreover, we use a terminal set constraint to recursively guarantee the existence of safe control actions at every iteration. In our experiments, we show that the resulting algorithm can be used to safely and efficiently explore and learn about dynamic systems.
