Model-based Validation as Probabilistic Inference
Harrison Delecki Anthony Corso Mykel J. Kochenderfer
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.