A domain-decomposed neural field predicts cost-to-go as a latent-space distance, enabling physics-informed motion planning in large and real-world environments.
Informed RRT: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,
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Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments
A domain-decomposed neural field predicts cost-to-go as a latent-space distance, enabling physics-informed motion planning in large and real-world environments.