Vision-language models are fundamentally changing how robots interact with humans in manufacturing environments, according to a new survey published in Frontiers of Engineering Management. The research examines how these AI systems that jointly process images and language are enabling robots to become flexible collaborators rather than scripted tools. The study, available at https://doi.org/10.1007/s42524-025-4136-9, analyzes 109 studies from 2020-2024 to provide the first comprehensive mapping of how VLMs are reshaping human-robot collaboration in smart manufacturing.
Traditional industrial robots have been limited by brittle programming, limited perception, and minimal understanding of human intent, making them poorly suited for dynamic factory environments. The survey demonstrates how VLMs add a powerful cognitive layer to robots through architectures based on transformers and dual-encoder designs. These models learn to align images and text through contrastive objectives, generative modeling, and cross-modal matching, creating shared semantic spaces that allow robots to understand both their environment and human instructions. This represents a significant departure from conventional rule-based systems that could not achieve this level of contextual understanding.
In practical applications, VLMs enable robots to interpret human commands, analyze real-time scenes, break down multi-step instructions, and generate executable action sequences. Systems built on models like CLIP, GPT-4V, BERT, and ResNet have achieved success rates above 90% in collaborative assembly and tabletop manipulation tasks. For navigation, VLMs allow robots to translate natural-language goals into movement, mapping visual cues to spatial decisions. These capabilities enable robots to follow detailed step-by-step instructions or reason from higher-level intent, providing robust autonomy in industrial environments.
The technology shows particular promise in manipulation tasks, where VLMs help robots recognize objects, evaluate affordances, and adjust to human motion—critical capabilities for safety-critical collaboration on factory floors. The review also highlights emerging work in multimodal skill transfer, where robots learn directly from visual-language demonstrations rather than requiring labor-intensive coding. This approach could significantly reduce the time and expertise needed to program industrial robots for new tasks.
Researchers emphasize that VLMs mark a turning point for industrial robotics by enabling a shift from scripted automation to contextual understanding. Robots equipped with these models can comprehend both what they see and what they are told, making interaction more intuitive and safer for human workers. The authors envision VLM-enabled robots becoming central to future smart factories, capable of adjusting to changing tasks, assisting workers in assembly, retrieving tools, managing logistics, conducting equipment inspections, and coordinating multi-robot systems.
Despite these advances, the survey cautions that achieving large-scale deployment will require addressing challenges in model efficiency, robustness, and data collection. Developing industrial-grade multimodal benchmarks for reliable evaluation will be crucial for real-world implementation. As VLMs mature, robots could learn new procedures from video-and-language demonstrations, reason through long-horizon plans, and collaborate fluidly with humans without extensive reprogramming. Breakthroughs in efficient VLM architectures, high-quality multimodal datasets, and dependable real-time processing will be key to unlocking their full industrial impact, potentially ushering in a new era of safe, adaptive, and human-centric manufacturing.
