Transformation of industrial robotics with natural language models: Recent progress and future prospects

Integration of Natural Language Models (NLMs) into industrial robots enhances operational efficiency and intuitive human-robot interactions, and thus it represents a significant opportunity in the pursuit of Industry 4.0/5.0. This paper provides a comprehensive survey on the technological advancements and applications in this area, by emphasizing their role in improving task execution, cognitive capabilities, and communication in the industrial environments. Meanwhile, related challenges are analyzed and discussed. In particular, NLMs inherently struggle with contextual understanding, which can lead to inappropriate or impractical outputs in complex industrial environments. Also, the external noise and the need for real-time responsiveness present further complications to the effectiveness of NLMs. Concerns regarding safety, transparency, privacy, and ethical usage amplify the need for regulatory considerations. In addition, standardized approaches to interpreting vague human instructions are called for to improve the interaction between humans and robots. It is pointed out that the broader impacts of NLMs can extend beyond industrial environments into commercial and social settings, thereby enhancing service quality and customer interactions. As a result, the review is expected to provide insights on how to effectively integrate NLMs with robotic systems, stimulate research to address the remaining challenges, and enhance transparency to improve social acceptability.