ShieldNN: 不安全なNNコントローラのための証明可能な安全フィルタ
ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers
キネマティック自転車モデルの障害物回避に対し、操舵・速度制約を直接扱う閉形式の制御バリア関数とシールドを設計し、RL訓練時のマルチ障害物安全行動フィルタとして有効性を示した論文。
著者: James Ferlez, Mahmoud Elnaggar, Yasser Shoukry, Cody Fleming
分類: cs.RO, cs.LG, cs.SY, eess.SY, math.OC
原文アブストラクト
In this paper, we develop a novel closed-form Control Barrier Function (CBF) and associated controller shield for the Kinematic Bicycle Model (KBM) with respect to obstacle avoidance. The proposed CBF and shield -- designed by an algorithm we call ShieldNN -- provide two crucial advantages over existing methodologies. First, ShieldNN considers steering and velocity constraints directly with the non-affine KBM dynamics; this is in contrast to more general methods, which typically consider only affine dynamics and do not guarantee invariance properties under control constraints. Second, ShieldNN provides a closed-form set of safe controls for each state unlike more general methods, which typically rely on optimization algorithms to generate a single instantaneous for each state. Together, these advantages make ShieldNN uniquely suited as an efficient Multi-Obstacle Safe Actions (i.e. multiple-barrier-function shielding) during training time of a Reinforcement Learning (RL) enabled Neural Network controller. We show via experiments that ShieldNN dramatically increases the completion rate of RL training episodes in the presence of multiple obstacles, thus establishing the value of ShieldNN in training RL-based controllers.
