ニューラル制御バリア関数の訓練法：複雑な入力制約系のための安全フィルタ学習
How to Train Your Neural Control Barrier Function: Learning Safety Filters for Complex Input-Constrained Systems
入力制約下の高次元システムでも安全を保証できるよう、名目方策の価値関数を学習して制御バリア関数を構成するPNCBFを提案し、F-16やクアッドコプターで有効性を示した。
著者: Oswin So, Zachary Serlin, Makai Mann, Jake Gonzales, Kwesi Rutledge, Nicholas Roy, Chuchu Fan
分類: math.OC, cs.RO
原文アブストラクト
Control barrier functions (CBF) have become popular as a safety filter to guarantee the safety of nonlinear dynamical systems for arbitrary inputs. However, it is difficult to construct functions that satisfy the CBF constraints for high relative degree systems with input constraints. To address these challenges, recent work has explored learning CBFs using neural networks via neural CBF (NCBF). However, such methods face difficulties when scaling to higher dimensional systems under input constraints. In this work, we first identify challenges that NCBFs face during training. Next, to address these challenges, we propose policy neural CBF (PNCBF), a method of constructing CBFs by learning the value function of a nominal policy, and show that the value function of the maximum-over-time cost is a CBF. We demonstrate the effectiveness of our method in simulation on a variety of systems ranging from toy linear systems to an F-16 jet with a 16-dimensional state space. Finally, we validate our approach on a two-agent quadcopter system on hardware under tight input constraints.
