arXiv (All)
open access
Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence
While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large language models (LLMs) and vision language models (VLMs), opens the possibility of teams that infer mission-relevant semantics and subtasks given high-level n
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
- Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams#792714
- Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams#788714
- Exploring Autonomous Agentic Data Engineering for Model Specialization#800049
- Atlas: Optimizing Deployment of Compound AI Workflows on Heterogeneous Clusters#927797
- CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic Environments#1037707
Record · ID 1036592
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