確率的推論としてのモデルベース検証
Model-based Validation as Probabilistic Inference
逐次システムの故障軌道分布推定をベイズ推論として定式化し、自動微分による軌道勾配とHMCサンプリングで効率的に検証する手法を提案した。
著者: Harrison Delecki, Anthony Corso, Mykel J. Kochenderfer
分類: cs.RO, cs.LG, stat.ML
原文アブストラクト
Estimating the distribution over failures is a key step in validating autonomous systems. Existing approaches focus on finding failures for a small range of initial conditions or make restrictive assumptions about the properties of the system under test. We frame estimating the distribution over failure trajectories for sequential systems as Bayesian inference. Our model-based approach represents the distribution over failure trajectories using rollouts of system dynamics and computes trajectory gradients using automatic differentiation. Our approach is demonstrated in an inverted pendulum control system, an autonomous vehicle driving scenario, and a partially observable lunar lander. Sampling is performed using an off-the-shelf implementation of Hamiltonian Monte Carlo with multiple chains to capture multimodality and gradient smoothing for safe trajectories. In all experiments, we observed improvements in sample efficiency and parameter space coverage compared to black-box baseline approaches. This work is open sourced.
