Summary
This paper introduces a novel training method, "Relaxation-Informed Training," for ReLU neural networks used as surrogate models. By embedding these networks into Mixed-Integer Linear Programs (MILPs), the method enables global optimization over the learned function. It specifically aims to improve the tractability of these MILPs by optimizing for structural properties like the number of binary variables and the tightness of the continuous LP relaxation during training, which standard objectives typically overlook.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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