LLMs for Text-Based Exploration and Navigation Under Partial Observability
📰 ArXiv cs.AI
Learn how to use LLMs for text-based exploration and navigation in unknown environments with partial observability
Action Steps
- Build a text-based controller using LLMs to navigate in unknown layouts
- Configure the LLM to function under partial observability without code execution or program synthesis
- Test the LLM controller in a reproducible benchmark with oracle localization in fixed ASCII gridworlds
- Apply the LLM controller to real-world scenarios such as inspection, logistics, and search-and-rescue
- Compare the performance of the LLM controller with other navigation methods
Who Needs to Know This
Researchers and developers working on AI and robotics can benefit from this knowledge to improve navigation and exploration in unknown environments
Key Insight
💡 LLMs can function as text-only controllers under partial observability without code execution or program synthesis
Share This
🤖 LLMs can be used for text-based exploration and navigation in unknown environments with partial observability! #LLMs #AI #Robotics
Key Takeaways
Learn how to use LLMs for text-based exploration and navigation in unknown environments with partial observability
Full Article
Title: LLMs for Text-Based Exploration and Navigation Under Partial Observability
Abstract:
arXiv:2604.09604v1 Announce Type: new Abstract: Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language models (LLMs) can function as \emph{text-only} controllers under partial observability -- without code execution, tools, or program synthesis. We introduce a reproducible benchmark with oracle localisation in fixed ASCII gridworlds: each step reveals only a local $5\times5$ window around the agent an
Abstract:
arXiv:2604.09604v1 Announce Type: new Abstract: Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language models (LLMs) can function as \emph{text-only} controllers under partial observability -- without code execution, tools, or program synthesis. We introduce a reproducible benchmark with oracle localisation in fixed ASCII gridworlds: each step reveals only a local $5\times5$ window around the agent an
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