arXiv (All)
open access
End-to-End Safe Social Navigation via Multi-Task Reinforcement Learning and Probabilistic Perception
Autonomous social navigation requires balancing efficiency, physical safety, and social compliance. Reinforcement Learning (RL) methods provide a viable and effective solution but often rely on unrealistic assumptions, such as the knowledge of humans' position and velocity. In this paper, we introdu
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
- A Unified Bellman Operator for Safety-Critical Reinforcement Learning#1249844
- A Unified Bellman Operator for Safety-Critical Reinforcement Learning#1242734
- Turning Safety into Competence: Minimally Exploitable Robot Policies via Safety-Filtered Reinforcement Learning#1063523
- RL-Guided PAC-NMPC for Probabilistically-Safe Perception-Based Navigation in Unknown Environments#1145021
Record · ID 1201746
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