ニューラルネットワーク到達可能性によるブラックボックス系の狭い隙間を通る到達回避保証
Guaranteed Reach-Avoid for Black-Box Systems through Narrow Gaps via Neural Network Reachability
ReLUニューラルネットで軌道データをモデル化し、モデル誤差を考慮した到達可能集合を計算することで、狭い隙間を通って目標到達と障害物回避を保証する手法NeuralPARCを提案。
著者: Long Kiu Chung, Wonsuhk Jung, Srivatsank Pullabhotla, Parth Shinde, Yadu Sunil, Saihari Kota, Luis Felipe Wolf Batista, Cédric Pradalier, Shreyas Kousik
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
In the classical reach-avoid problem, autonomous mobile robots are tasked to reach a goal while avoiding obstacles. However, it is difficult to provide guarantees on the robot's performance when the obstacles form a narrow gap and the robot is a black-box (i.e. the dynamics are not known analytically, but interacting with the system is cheap). To address this challenge, this paper presents NeuralPARC. The method extends the authors' prior Piecewise Affine Reach-avoid Computation (PARC) method to systems modeled by rectified linear unit (ReLU) neural networks, which are trained to represent parameterized trajectory data demonstrated by the robot. NeuralPARC computes the reachable set of the network while accounting for modeling error, and returns a set of states and parameters with which the black-box system is guaranteed to reach the goal and avoid obstacles. NeuralPARC is shown to outperform PARC, generating provably-safe extreme vehicle drift parking maneuvers in simulations and in real life on a model car, as well as enabling safety on an autonomous surface vehicle (ASV) subjected to large disturbances and controlled by a deep reinforcement learning (RL) policy.
