Training and sampling in static scalar energy generative models are two instances of the same Lyapunov-driven density transport dynamics on Wasserstein space, differing only by initial condition, which yields a finite stopping criterion for Langevin sampling and additive composition rules that keep
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A reactive planning strategy locally adjusts reference paths for collision avoidance while an adaptive tracking controller follows the changes via discretization, enabling safe robot navigation in cluttered environments with partial information.
citing papers explorer
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Energy Generative Modeling: A Lyapunov-based Energy Matching Perspective
Training and sampling in static scalar energy generative models are two instances of the same Lyapunov-driven density transport dynamics on Wasserstein space, differing only by initial condition, which yields a finite stopping criterion for Langevin sampling and additive composition rules that keep
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Reactive Planning based Control for Mobile Robots in Obstacle-Cluttered Environments
A reactive planning strategy locally adjusts reference paths for collision avoidance while an adaptive tracking controller follows the changes via discretization, enabling safe robot navigation in cluttered environments with partial information.