Counterexample-Guided Synthesis of Perception Models and Control

Understand

Recent advances in learning-based perception systems have led to drastic improvements in the performance of robotic systems like autonomous vehicles and surgical robots.

• These perception systems, however, are hard to analyze and errors in them can propagate to cause catastrophic failures.
• In this paper, we consider the problem of synthesizing safe and robust controllers for robotic systems which rely on complex perception modules for feedback.
• We propose a counterexample-guided synthesis framework that iteratively builds simple surrogate models of the complex perception module and enables us to find safe control policies.