BarrierNet：安全性を保証するニューラルネットワーク層
BarrierNet: A Safety-Guaranteed Layer for Neural Networks
環境に応じて安全性制約を適応させられる微分可能な高次制御バリア関数をニューラルネットワークと一緒に学習できる安全層として提案し、交通合流やロボットナビゲーションで有効性を示した論文。
著者: Wei Xiao, Ramin Hasani, Xiao Li, Daniela Rus
分類: cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper introduces differentiable higher-order control barrier functions (CBF) that are end-to-end trainable together with learning systems. CBFs are usually overly conservative, while guaranteeing safety. Here, we address their conservativeness by softening their definitions using environmental dependencies without loosing safety guarantees, and embed them into differentiable quadratic programs. These novel safety layers, termed a BarrierNet, can be used in conjunction with any neural network-based controller, and can be trained by gradient descent. BarrierNet allows the safety constraints of a neural controller be adaptable to changing environments. We evaluate them on a series of control problems such as traffic merging and robot navigations in 2D and 3D space, and demonstrate their effectiveness compared to state-of-the-art approaches.
