Bridging Dimensions: Confident Reachability for High-Dimensional Controllers
This work addresses the problem of scalable verification for autonomous systems with high-dimensional inputs, which is incremental as it builds on existing verification and distillation techniques.
The paper tackles the challenge of verifying high-dimensional, learning-based controllers by approximating them with several low-dimensional controllers using verification-aware knowledge distillation, and it demonstrates convincing performance in three OpenAI gym benchmarks.
Autonomous systems are increasingly implemented using end-to-end learning-based controllers. Such controllers make decisions that are executed on the real system, with images as one of the primary sensing modalities. Deep neural networks form a fundamental building block of such controllers. Unfortunately, the existing neural-network verification tools do not scale to inputs with thousands of dimensions -- especially when the individual inputs (such as pixels) are devoid of clear physical meaning. This paper takes a step towards connecting exhaustive closed-loop verification with high-dimensional controllers. Our key insight is that the behavior of a high-dimensional controller can be approximated with several low-dimensional controllers. To balance the approximation accuracy and verifiability of our low-dimensional controllers, we leverage the latest verification-aware knowledge distillation. Then, we inflate low-dimensional reachability results with statistical approximation errors, yielding a high-confidence reachability guarantee for the high-dimensional controller. We investigate two inflation techniques -- based on trajectories and control actions -- both of which show convincing performance in three OpenAI gym benchmarks.