Vision-language models are reshaping how humans and robots collaborate in manufacturing environments, according to a new survey published in Frontiers of Engineering Management. The research, conducted by a team from The Hong Kong Polytechnic University and KTH Royal Institute of Technology, provides the first comprehensive mapping of how these AI systems are enabling robots to become flexible collaborators rather than scripted tools.
The survey, which analyzed 109 studies from 2020-2024, demonstrates how vision-language models—AI systems that jointly process images and language—allow robots to plan tasks, navigate complex environments, perform manipulation, and learn new skills directly from multimodal demonstrations. Traditional industrial robots have been constrained by brittle programming and limited perception, struggling to adapt to dynamic factory environments where human intent and changing conditions require constant adjustment.
According to the research published at https://doi.org/10.1007/s42524-025-4136-9, vision-language models 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 enable robots to understand both their environments and human instructions simultaneously.
In practical applications, systems built on models like CLIP, GPT-4V, BERT, and ResNet achieve success rates above 90% in collaborative assembly and tabletop manipulation tasks. For task planning, vision-language models help robots interpret human commands, analyze real-time scenes, break down multi-step instructions, and generate executable action sequences. In navigation, they allow robots to translate natural-language goals into movement by mapping visual cues to spatial decisions, enabling robust autonomy in domestic, industrial, and embodied environments.
The technology's impact on manipulation is particularly significant for factory safety, as vision-language models help robots recognize objects, evaluate affordances, and adjust to human motion. The survey also highlights emerging work in multimodal skill transfer, where robots learn directly from visual-language demonstrations rather than labor-intensive coding, potentially reducing implementation time and increasing flexibility.
The authors emphasize that vision-language models represent a turning point for industrial robotics by enabling a shift from scripted automation to contextual understanding. "Robots equipped with VLMs can comprehend both what they see and what they are told," they explain, noting that this dual-modality reasoning makes interaction more intuitive and safer for human workers. However, achieving large-scale deployment will require addressing challenges in model efficiency, robustness, and data collection, as well as developing industrial-grade multimodal benchmarks for reliable evaluation.
Looking forward, the researchers envision vision-language-model-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. As these models mature, robots could learn new procedures from video-and-language demonstrations, reason through long-horizon plans, and collaborate fluidly with humans without extensive reprogramming. The authors conclude that breakthroughs in efficient vision-language model 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.
