AI RESEARCH
RAYEN: Imposition of Hard Convex Constraints on Neural Networks
arXiv CS.LG
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ArXi:2307.08336v2 Announce Type: replace Despite the numerous applications of convex constraints in Robotics, enforcing them within learning-based frameworks remains an open challenge. Existing techniques either fail to guarantee satisfaction at all times, or incur prohibitive computational costs. This paper presents RAYEN, a framework for imposing hard convex constraints on the output or latent variables of a neural network. RAYEN guarantees constraint satisfaction during both