Energy-structuring a latent world model with port-Hamiltonian dynamics and conditioning Eikonal neural time fields on its predictions improves open-world navigation success and physical collision rate in simulation, though the gain over a generic world model within the same planner is modest.
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Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning
Energy-structuring a latent world model with port-Hamiltonian dynamics and conditioning Eikonal neural time fields on its predictions improves open-world navigation success and physical collision rate in simulation, though the gain over a generic world model within the same planner is modest.