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GP-CBF is presented, a learning-based framework for synthesizing and continuously adapting a robust control barrier value function for safe robot navigation in unknown environments, ensuring safety and outperforming existing baselines.
Bounded navigation-aware retention preserves navigation performance while considerably reducing GFM-KV memory relative to larger-memory temporal retention, which support jointly examining the geometric representations exposed to the policy and the historical evidence retained for future inference.
An Octree REsidual Network (OREN) is developed that pairs an explicit octree prior with an implicit neural residual to reconstruct SDFs online from point cloud observations with the efficiency of volumetric methods and the accuracy and differentiability of neural methods.
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