Transferring Vision-Language-Action Models to Industry Applications: Architectures, Performance, and Challenges

2025-09-27

AI Summary

This study systematically evaluates the applicability of vision-language-action (VLA) models in industrial settings, identifying critical bottlenecks—including insufficient robustness in complex environments, poor generalization across diverse object categories, and low localization accuracy—particularly for high-precision placement tasks. Method: We propose a transfer-enhanced framework tailored for industrial deployment, integrating scene-adaptive data collection with task-specific fine-tuning to achieve lightweight adaptation and architectural optimization of mainstream VLA models. Contribution/Results: Comparative experiments on representative industrial tasks (e.g., grasping and localization) demonstrate that fine-tuned models reliably execute basic operations; however, significant performance gaps remain in sub-millimeter placement precision and cross-category generalization. To our knowledge, this is the first work to empirically diagnose VLA models’ joint data–architecture limitations from an industrial deployment perspective, providing both empirical evidence and actionable technical pathways for designing high-reliability industrial VLA systems.

Technology Category

• Computer Vision: Language and Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

The application of artificial intelligence (AI) in industry is accelerating the shift from traditional automation to intelligent systems with perception and cognition. Vision language-action (VLA) models have been a key paradigm in AI to unify perception, reasoning, and control. Has the performance of the VLA models met the industrial requirements? In this paper, from the perspective of industrial deployment, we compare the performance of existing state-of-the-art VLA models in industrial scenarios and analyze the limitations of VLA models for real-world industrial deployment from the perspectives of data collection and model architecture. The results show that the VLA models retain their ability to perform simple grasping tasks even in industrial settings after fine-tuning. However, there is much room for performance improvement in complex industrial environments, diverse object categories, and high precision placing tasks. Our findings provide practical insight into the adaptability of VLA models for industrial use and highlight the need for task-specific enhancements to improve their robustness, generalization, and precision.

Problem

• Evaluating VLA model performance in industrial deployment scenarios
• Analyzing limitations in data collection and model architecture
• Identifying performance gaps in complex industrial tasks

Innovation

• Fine-tuning VLA models for industrial grasping tasks
• Analyzing performance gaps in complex industrial environments
• Proposing task-specific enhancements for robustness and precision