arXiv (All)
open access
SQ-CBF: Signed Distance Functions for Reliable Superquadric-Based Safety Filtering
Ensuring safe robot operation in cluttered and dynamic environments remains a fundamental challenge. While control barrier functions (CBFs) provide an effective framework for real-time safety filtering, their performance critically depends on the underlying geometric representation, which is often s
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
- VertexCBF: Improving Neural Control Barrier Functions via Vertex-Restricted Control Search#1210061
- Learning Safe-by-Design Neural Network Controllers#1052178
- Safety Under State Uncertainty: Robustifying Control Barrier Functions#1013435
- Gradient-Free Neural Hamilton-Jacobi Reachability for Scalable Safety-Critical Control#1083561
- A Recursive CBF Framework for Safety under State Uncertainty#1012464
Record · ID 1326203
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