Dual-Agent Framework for Cross-Model Verified Translation of Natural-Language Protocols into Robotic Laboratory Platform
Researchers have developed a dual-agent framework to translate natural-language biological experiment protocols into executable commands for robotic laboratory platforms. The system uses a Parser Agent to formalize protocols and a rule-based mapping engine to generate device-level commands. An LLM Validation Agent then verifies the accuracy and completeness of these commands, initiating a self-correction loop if errors are found. This approach aims to bridge the semantic gap between human-readable protocols and automated laboratory systems, as demonstrated by its successful application in autonomous execution of protein quantification experiments. AI
IMPACT This framework could significantly accelerate scientific discovery by enabling more autonomous and efficient laboratory automation.