  Assistive robotic devices are a particularly promising field of application
for neural networks (NN) due to the need for personalization and hard-to-model
human-machine interaction dynamics. However, NN based estimators and
controllers may produce potentially unsafe outputs over previously unseen data
points. In this paper, we introduce an algorithm for updating NN control
policies to satisfy a given set of formal safety constraints, while also
optimizing the original loss function. Given a set of mixed-integer linear
constraints, we define the NN repair problem as a Mixed Integer Quadratic
Program (MIQP). In extensive experiments, we demonstrate the efficacy of our
repair method in generating safe policies for a lower-leg prosthesis.
