AINeutralarXiv – CS AI · 5h ago6/10
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Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints
Researchers present a neuro-symbolic learning framework that addresses a critical inefficiency in robotic task planning by combining neural networks with symbolic planning under complex logical constraints. The method uses bilevel optimization to learn object-importance scores while solving planning problems in pruned search spaces, reducing planning failures by 80% and planning time by 57% across multiple benchmarks and real-world robotic applications.