A Multi-Agentic Digital Twin Framework for Human-in-the-loop Workflows

Traditional robot task planning struggles to recover from failures and changing requirements because it relies on predefined rules, labelled datasets, and limited state awareness. Digital twins provide real-time virtual representations of physical systems, while multi-agent systems support collaborative, stepwise reasoning. However, their integration for failure evaluation, requirement adaptation, and state-aware robot planning remains underexplored, particularly at the architectural level. This study proposes a multi-agentic digital twin framework for human-in-the-loop workflows, enabling bidirectional communication between machines, digital twins, and multi-agents. The framework is validated through robot task planning for an aerospace product-qualification metrology process. Results show that digital-twin integration adds 885-1,149 milliseconds to communication between robots and agents because continuous state updates are required. Multi-agent workflows need more human feedback than single-agent workflows to align outputs with predefined goals and mitigate hallucinations, highlighting the importance of human-in-the-loop orchestration. Datasets, logs, code, videos, results, and deployment instructions are available on GitHub 1 .