Robust Counterexample-guided Optimization for Planning from Differentiable Temporal Logic

Dawson, Charles; Fan, Chuchu

doi:10.1109/iros47612.2022.9981382

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Cited by 4 publications

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Verification-guided Programmatic Controller Synthesis

Wang

Zhu

2023

Lecture Notes in Computer Science

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We present a verification-based learning framework VEL that synthesizes safe programmatic controllers for environments with continuous state and action spaces. The key idea is the integration of program reasoning techniques into controller training loops. VEL performs abstraction-based program verification to reason about a programmatic controller and its environment as a closed-loop system. Based on a novel verification-guided synthesis loop for training, VEL minimizes the amount of safety violation in the proof space of the system, which approximates the worst-case safety loss, using gradient-descent style optimization. Experimental results demonstrate the substantial benefits of leveraging verification feedback for synthesizing provably correct controllers.

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