Dual-Agent Framework for Cross-Model Verified Translation of Natural-Language Protocols into Robotic Laboratory Platform
arXiv:2606.20120v1 Announce Type: cross Abstract: Biological experiment protocols are written in natural language, whereas automation systems rely on predefined control commands, creating a semantic gap that limits autonomous execution. Microplate-based automatic experiments are particularly challenging due to the need to simultaneously control well mapping, sample-reagent combinations, replicate placement, and parallel dispensing. This study proposes an agent-based protocol translation framework that converts natural-language microplate-based protocols into executable control commands for a r
The increasing sophistication of AI models and the demand for autonomous laboratory environments are converging, enabling the development of advanced robotic control systems.
This development allows for greater automation and reproducibility in biological experiments, accelerating scientific discovery and reducing human error in complex lab procedures.
Biological research labs can transition from manual, natural language-driven protocols to fully autonomous, AI-driven robotic execution, significantly boosting efficiency and throughput.
- · Biotechnology and pharmaceutical companies
- · Robotics manufacturers
- · AI software developers
- · Academic research institutions
- · Manual lab technicians (some roles)
- · Traditional lab automation vendors (if they don't adapt)
Increased pace and scale of biological experimentation.
Reduced cost and time for drug discovery and material science innovation.
The democratization of complex biological research, as AI agents handle intricate experimental design and execution.
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Read at arXiv cs.AI