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LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials

Paper recorded by Signals 4 on 2026-09-23 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-23 on arXiv · recorded by Signals 4 on 2026-09-24

Category: cs.LG · 机器学习 · first seen 2026-09-24

Abstract

Control barrier functions (CBF) are a popular safety filter to ensure safety for nonlinear dynamical systems. However, when the system is subject to uncertainties and disturbances, this requires the use of robust variants of CBFs, which can be difficult to construct and can be overly conservative, especially for high-dimensional systems under input constraints. In this work, we propose a new appro

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#13 most recent of 278 cs.LG papers we have recorded · ↑ newer: ForgetMimic: Motion Unlearning for Reinforcement Learning Humanoid Con · ↓ older: Local Geometric Mixing via Dobrushin Contraction with Applications to
Cite this page: LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials: the #13 most recent of 278 cs.LG papers we have recorded (as of 2026-09-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/leap-cbf-a-safety-filter-for-uncertain-systems-with-least-effort-adversarial-pot.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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