arXiv (All)
open access
Safe Task Planning with Long-Term Graph Memory for Embodied Agents
Large language models (LLMs) and vision-language models (VLMs) have significantly advanced zero-shot task planning for embodied agents. However, most LLM- and VLM-driven methods struggle to generate safe high-level actions due to a lack of physical risk awareness, particularly under partial observab
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
- ProgressCompass: Embodied Progress Reward Models Are Lost Without the Right Context#1131885
- Says Block, Still Acts: Why LLM Safety Judgments Fail to Govern Action in LLM Agents#1130712
- COUNTERMEM: World-Model Verified Counter-Factual Memory for Language Agents#1115830
- Memory as Plans: World-Action Modeling with Memory-Grounded Planning#999082
- Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents#610965
Record · ID 985797
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