Human-robot interaction: predicting research agenda by long short-term memory

• Published in: PeerJ. Computer science 2024-10, Vol.10, p.e2335, Article e2335
• Main Authors: Borregan-Alvarado, Jon, Alvarez-Meaza, Izaskun, Cilleruelo-Carrasco, Ernesto, Rio-Belver, Rosa Maria
• Format: Article
• Language: English
• Subjects: Computational linguistics; Data mining; Forecasts and trends; Human–robot interaction; Innovations; Language processing; Long short-term memory; Natural language interfaces; Natural language processing; Network analysis; Neural networks; Recurrent neural network; Research agenda; Robotics; Robots
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.2335

The article addresses the identification and prediction of research topics in human-robot interaction (HRI), fundamental in Industry 4.0 (I4.0) and future Industry 5.0 (I5.0). In the absence of research agendas in the scientific literature, the study proposes a multilayered model to create a precise agenda to guide the scientific community in new developments in collaborative robotics and HRI technologies. The methodology is divided into four stages, which make up the three layers of the model. In the first two stages, scientific articles on HRI for the period 2020-2021 were collected and analyzed using data mining techniques together with VantagePoint and Gephi software to identify keywords and their relationships. These initial stages form layer 1 of the model, where the main scientific themes are recognized. In the third stage, article titles and abstracts are cleaned and processed using natural language processing (NLP) techniques, generating word embeddings models that highlight relevant HRI-related terms, forming layer 2. The fourth and final stage uses Recurrent Neural Networks (RNN) with long short-term memory (LSTM) architecture to predict future topics, consolidating the previously identified terms and forming layer 3 of the model. The results show that in layer 1 HRI has intensive application in various sectors through advanced computational algorithms, with trust as a key feature. In layer 2, terms such as vision, sensors, communication, collaboration and anthropomorphic aspects are fundamental, while layer 3 anticipates future topics such as design, performance, method and controllers, essential to improve robot interaction. The study concludes that the methodology is effective in defining a robust and relevant research agenda. By identifying future trends and needs, this work fills a gap in the scientific literature, providing a valuable tool for the research community in the field of HRI.