arXiv (All)
open access
LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials
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, e
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Safety Under State Uncertainty: Robustifying Control Barrier Functions#1013435
- Learning Safe-by-Design Neural Network Controllers#1052178
- Provably Optimal Reinforcement Learning under Safety Filtering#1034680
- Layered Safety: Enhancing Autonomous Collision Avoidance via Multistage CBF Safety Filters#999742
- Predicted-Flow Control Barrier Functions for Real-Time Safe Optimal Control#1049858
Record · ID 1064967
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive