Counterexample-Guided Synthesis of Perception Models and Control
This addresses safety-critical issues in robotics like autonomous vehicles and surgical robots, but it is incremental as it builds on existing synthesis and falsification methods.
The paper tackles the problem of synthesizing safe controllers for robotic systems that rely on complex perception modules, which are prone to errors that can cause failures, by proposing a counterexample-guided framework that iteratively builds surrogate models and finds robust control policies. The result is demonstrated in simulation for lane keeping and automatic braking scenarios, showing that the framework generates safe controllers and simpler models of deep neural network-based perception systems.
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. The framework uses a falsifier to find counterexamples, or traces of the systems that violate a safety property, to extract information that enables efficient modeling of the perception modules and errors in it. These models are then used to synthesize controllers that are robust to errors in perception. If the resulting policy is not safe, we gather new counterexamples. By repeating the process, we eventually find a controller which can keep the system safe even when there is a perception failure. We demonstrate our framework on two scenarios in simulation, namely lane keeping and automatic braking, and show that it generates controllers that are safe, as well as a simpler model of a deep neural network-based perception system that can provide meaningful insight into operations of the perception system.