Hybrid Agentic AI-FSM Framework for Instruction-Based Industrial Manipulation Tasks

Abstract

This paper proposes a hybrid Agentic AI–FSM framework for robust natural-language-driven automation in safety-critical industrial robotics applications. Although natural-language procedures are commonplace in manufacturing, translating them into reliable robot programs remains labor-intensive. While Large Language Models (LLMs) offer strong parsing and planning capabilities, their inherent non-determinism and susceptibility to hallucinations preclude their direct use for robot control. To bridge this gap, our architecture employs an LLM-based planning agent to translate instructions offline into a structured task plan. Execution is then delegated to a deterministic Finite State Machine (FSM)-style execution engine to ensure reliability. Safety is further guaranteed by a multi-stage validation–simulation pipeline that verifies schema compliance and operational constraints through dry runs prior to deployment. For runtime anomalies, a RAG-enhanced Exception Handling Agent proposes recovery options, which are strictly mediated through a human-in-the-loop (HIL) interface for operator approval. Finally, a rule-based Safety Agent enforces physical constraints and provides an independent protection layer.