Latent Barrier Steering: Hierarchical Safety for Generative Planning
Overview
This research introduces Latent Barrier Steering (LBS), a hierarchical semantic-to-path framework designed to enhance safety in flow-matching-based generative planning. Unlike existing control-barrier-function (CBF) filters that act locally on generated samples, LBS decouples behavior selection from path certification by steering the generator's behavior latent toward rollouts with larger safety margins before applying a path-space CBF quadratic program only for residual violations. Experiments across navigation and robot end-effector planning tasks demonstrate that LBS preserves safety while improving goal reaching and reducing correction burden compared with traditional path-level safety filters.
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- arXiv Robotics — research abstractsSignalLatent Barrier Steering: Hierarchical Safety for Generative Planning
This research introduces Latent Barrier Steering (LBS), a hierarchical semantic-to-path framework designed to enhance safety in flow-matching-based generative planning. Unlike existing control-barrier-function (CBF) filters that act locally on generated samples, LBS decouples behavior selection from path certification by steering the generator's behavior latent toward rollouts with larger safety margins before applying a path-space CBF quadratic program only for residual violations. Experiments across navigation and robot end-effector planning tasks demonstrate that LBS preserves safety while improving goal reaching and reducing correction burden compared with traditional path-level safety filters.
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