A verification layer based on temporal logic and discrete event systems ensures that LLM-generated task allocations in multi-robot manufacturing remain safe.
Lang2ltl: Translating natural language commands to temporal specification with large language models,
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OATH combines adaptive Halton sampling, obstacle-aware clustering with auctions, and LLM-based instruction interpretation to improve task assignment and planning for heterogeneous robot teams in obstacle-rich environments.
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Logic-Based Verification of Task Allocation for LLM-Enabled Multi-Agent Manufacturing Systems
A verification layer based on temporal logic and discrete event systems ensures that LLM-generated task allocations in multi-robot manufacturing remain safe.
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Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming
OATH combines adaptive Halton sampling, obstacle-aware clustering with auctions, and LLM-based instruction interpretation to improve task assignment and planning for heterogeneous robot teams in obstacle-rich environments.