RAYEN: Imposition of Hard Convex Constraints on Neural Networks

Understand

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 training and testing, for any input and any network weights.