KiTe augments AO-RRT with terminal costs and belief-space Wasserstein minimization to improve goal-reaching reliability under learned uncertainty while preserving asymptotic optimality.
Learning to poke by poking: Experiential learning of intuitive physics
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Terminal Matters: Kinodynamic Planning with a Terminal Cost and Learned Uncertainty in Belief State-Cost Space
KiTe augments AO-RRT with terminal costs and belief-space Wasserstein minimization to improve goal-reaching reliability under learned uncertainty while preserving asymptotic optimality.