IEI-TIA: Industrial Embodied Intelligence Trustworthy Interpretable Agent for Robotic Long-Horizon and Repetitive Tasks

With the rapid advancement of generative artificial intelligence, embodied intelligence is increasingly being integrated into robotic tasks within smart manufacturing. Industrial operations typically feature long-horizon sequences and demand high precision. However, although embodied models, particularly Vision-Language-Action (VLA) models, have shown significant promise in autonomous control with superior generalization capabilities, their inherent black-box nature lacks the transparency and trustworthiness essential for manufacturing tasks. To address this challenge, we propose the Industrial Embodied Intelligence Trustworthy Interpretable Agent (IEI-TIA), a high-level trustworthy supervisory agent designed for industrial sorting and palletizing tasks. Rather than directly generating low-level control commands, IEI-TIA monitors robotic execution, diagnoses possible failure types at key operational stages, and provides corrective next-step instruction via invoked skills, thereby improving the reliability and interpretability of long-horizon and repetitive robotic tasks. An alignment-enhanced parameter-efficient finetuning method is proposed utilizing paired vision-language data to enable robust trustworthiness identification across various possible robotic failure types in different subtasks in sorting and palletizing. Additionally, we construct a specialized dataset and establish benchmarks for industrial embodied intelligence behavior diagnosis and instructions. Experimental results demonstrate that the fine-tuned trustworthy agent achieves 93.7% diagnosis accuracy for various task failures, representing a substantial improvement of approximately 38% over baseline general-purpose large models. By integrating the agent’s step-by-step operational instructions, the success rate of robotic long-horizon and repetitive tasks is improved by 14.0%.