arXiv (All)
open access
DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles
Robots operating in human-centered environments must safely navigate among multiple dynamic obstacles to avoid collisions with people and surrounding infrastructure. Control barrier functions (CBFs) provide an effective mechanism for safety filtering, and recent CBF-based reinforcement learning (RL)
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
- Provably Optimal Reinforcement Learning under Safety Filtering#1034680
- Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation#1104441
- Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)#1076118
- SAFE AND AGILE UAV NAVIGATION IN CONFINED AND TIGHT SPACES VIA DEEP REINFORCEMENT LEARNING#211397
- Density-Driven Area Coverage for Nonholonomic Multi-Robot Systems with Safety Guarantee#1037351
Record · ID 1143474
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