2021
DOI: 10.48550/arxiv.2104.02788
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Safe-by-Repair: A Convex Optimization Approach for Repairing Unsafe Two-Level Lattice Neural Network Controllers

Abstract: In this paper, we consider the problem of repairing a data-trained Rectified Linear Unit (ReLU) Neural Network (NN) controller for a discrete-time, input-affine system. That is we assume that such a NN controller is available, and we seek to repair unsafe closed-loop behavior at one known "counterexample" state while simultaneously preserving a notion of safe closed-loop behavior on a separate, verified set of states. To this end, we further assume that the NN controller has a Two-Level Lattice (TLL) architect… Show more

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Cited by 1 publication
(2 citation statements)
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“…These methods augment their networks with constrained optimization, but are unable to guarantee constraint satisfaction upon convergence of the training. Alternatively, [17] uses a systematic process of small changes to conform a "mostly-correct" network to constraints. However the method only works for networks with a Two-Level Lattice (TLL) architecture, requires an already-trained network, and again does not guarantee a provably safe solution.…”
Section: A Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…These methods augment their networks with constrained optimization, but are unable to guarantee constraint satisfaction upon convergence of the training. Alternatively, [17] uses a systematic process of small changes to conform a "mostly-correct" network to constraints. However the method only works for networks with a Two-Level Lattice (TLL) architecture, requires an already-trained network, and again does not guarantee a provably safe solution.…”
Section: A Related Workmentioning
confidence: 99%
“…We can then solve (17) for the Jacobian of v * with respect to any entry of the zonotope centers or generators by setting the right-hand side appropriately (see [22] for details). That is, we can now differentiate (14) with respect to the elements of c 1 , G 1 , c 2 , or G 2 .…”
Section: Differentiating the Collision Check Lossmentioning
confidence: 99%