unveiling-the-heuristic-core-in-pretrained-language-models

reconstructing-batches-in-federated-learning

guide-guidance-based-incremental-learning-with-diffusion-models

analyzing-music-difficulty-from-audio-recordings-insights-from-piano-syllabus-dataset

real-to-sim-to-real-approach-for-robust-manipulation

improving-deep-rl-scalability-with-classification-for-value-functions

bridging-language-and-items-for-retrieval-and-recommendation-blair-pretrained-models

backtracing-understanding-the-cause-of-user-queries


The core message of this paper is to learn safe output feedback control laws for unknown systems with uncertain dynamics and state estimation errors by constructing robust output control barrier functions (ROCBFs) from expert driving demonstrations.


coremsg

learning-safe-control-laws-from-expert-driving-demonstrations-with-uncertain-dynamics-and-state-estimation


Learning Safe Control Laws from Expert Driving Demonstrations with Uncertain Dynamics and State Estimation


title_rewrite

Robust Output Control Barrier Functions for Safe Autonomous Driving