Counterexample-Guided Synthesis of Perception Models and Control
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We consider the problem of synthesizing safe and robust controllers for real world robotic systems like autonomous vehicles, which rely on complex perception modules. We propose a counterexample-guided synthesis framework which iteratively learns perception models that enable finding safe control policies. We use counterexamples to extract information relevant for modeling the errors in perception modules. Such models then can be used to synthesize controllers 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 perception failure. Finally, we show that our framework computes robust controllers for autonomous vehicles in two different simulated scenarios: (i) lane keeping, and (ii) automatic braking.
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