Vision-Language-Action Intelligence for Traditional Industrial Robotic Arms

Vision-language-action (VLA) intelligence provides a unified route for connecting visual perception, language understanding, and executable control in traditional industrial robotic arms. This paper examines the transition from modular perception-planning-control pipelines to generalist policies that predict robot actions from multimodal observations and instructions. It reviews representative architectures, including RT-1, RT-2, Octo, and OpenVLA, and compares three major action-representation strategies: discrete action tokens, continuous action prediction, and specialised policy heads. Particular attention is given to the role of large-scale robot datasets, vision-language pre-training, action tokenization, and embodiment-specific adaptation. The analysis further considers the progression from multimodal intelligence to embodied intelligence and distinguishes cross-task, cross-environment, and cross-embodiment generalization. Existing results indicate that shared backbones and heterogeneous trajectory data can reduce the cost of adapting policies to new tasks and robot platforms, but reliable industrial deployment still depends on control precision, inference latency, safety constraints, data consistency, and closed-loop feedback. The paper concludes that practical VLA systems for industrial arms will require coordinated advances in data standards, model efficiency, controller integration, and hardware-aware evaluation.