Learning Feasibility

Learning feasibility focuses on determining whether a solution exists for a given problem, a crucial step in various fields like robotics, optimization, and machine learning. Current research emphasizes developing efficient algorithms and models, including neural networks (e.g., Transformers, Normalizing Flows), to predict feasibility, often leveraging limited data or incorporating prior knowledge (e.g., roadmaps, learned constraints). This research is significant because efficiently determining feasibility accelerates problem-solving in complex systems, improving the performance of applications ranging from robotic manipulation and autonomous driving to power grid management and federated learning.