Transferring Vision-Language-Action Models to Industry Applications: Architectures, Performance, and Challenges
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918149675286528 |
|---|---|
| author | Li, Shuai Yizhe, Chen Dong, Li Sichao, Liu Dapeng, Lan Yu, Liu Pang, Zhibo |
| author_facet | Li, Shuai Yizhe, Chen Dong, Li Sichao, Liu Dapeng, Lan Yu, Liu Pang, Zhibo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23121 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transferring Vision-Language-Action Models to Industry Applications: Architectures, Performance, and Challenges Li, Shuai Yizhe, Chen Dong, Li Sichao, Liu Dapeng, Lan Yu, Liu Pang, Zhibo Artificial Intelligence 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. |
| title | Transferring Vision-Language-Action Models to Industry Applications: Architectures, Performance, and Challenges |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.23121 |