Natural language processing-based development of artificial intelligence-driven autonomous socially assistive robots

Abstract

This study focuses on addressing the growing need for localized language support in socially assistive robots (SARs) due to rising labor costs and the limitations of human labor in developed countries. The research aims to develop a Turkish natural language processing (NLP) module to enhance SARs’ social interaction capabilities and integration into smart living spaces. By leveraging advanced machine learning models, specifically XLM-RoBERTa Large (deepset/xlm-roberta-large-squad2), the study evaluated cross-lingual transfer learning for Turkish question answering, addressing specific linguistic challenges, including agglutinative morphology and vowel harmony. The model was evaluated on the Turkish Question-Answering Dataset (TQuAD 2.0) with 2,520 validation examples, achieving 79.37% F1-score and 56.67% exact match score. The research established a methodological framework connecting adaptive NLP design principles with control systems theory, demonstrating how concepts from adaptive fuzzy control and robust neural adaptive control inform the development of more stable and reliable NLP systems for SAR applications. These outcomes highlight the potential of cross-lingual NLP models for SAR applications in Turkish-speaking environments. The research contributes to the field by: (i) evaluating cross-lingual transfer learning for Turkish SAR applications, (ii) demonstrating the effectiveness of XLM-RoBERTa for low-resource language adaptation, (iii) establishing a framework that connects adaptive NLP design with control systems theory for enhanced robustness, and (iv) identifying real-world SAE applications in healthcare, smart homes, and industrial settings. Future work will focus on integrating this NLP module with speech recognition and synthesis components for complete voice-interactive SAR systems.

Keywords

Socially assistive robot / Natural language processing / Large language model / Question-answering / XLM-RoBERTa / Turkish Question-Answering Dataset / Cross-lingual transfer learning / Adaptive control systems

Funding

This research was supported by internal funding from OSTİM Technical University Scientific Research Programs (Grant No. BAP202301).

References

[1] Alkesaiberi, A., Alkhathlan, A., & Abdelali, A. (2024). Ara-CANINE: Character-based pre-trained language model for Arabic language understanding. International Journal on Cybernetics & Informatics, 13(2), 45-59. https://doi.org/10.5121/ijci.2024.130204

[2] Boulkroune, A., Hamel, S., Zouari, F., Boukabou, A., & Ibeas, A. (2017). Output-feedback controller based projective lag-synchronization of uncertain chaotic systems in the presence of input nonlinearities. Mathematical Problems in Engineering, 2017, Article 8045803. https://doi.org/10.1155/2017/8045803

[3] Breazeal, C. (2017). Social robots: From research to commercialization. In: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction; March 6-9, 2017; Vienna, Austria. https://doi.org/10.1145/2909824.3020258

[4] Conneau, A., Khandelwal, K., Goyal, N., et al. (2020). Unsupervised cross-lingual representation learning at scale. In: Jurafsky, D., Chai, J., Schluter, N., & Tetreault, J., (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics; July 5-10, 2020; Online. pp. 8440-8451. https://doi.org/10.18653/v1/2020.acl-main.747

[5] Cui, Y., Liu, T., Che, W., et al. (2019). A span-extraction dataset for Chinese machine reading comprehension. In: Inui, K., Jiang, J., Ng, V., & Wan, X. (Eds.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP); November 3-7, 2019; Hong Kong, China, pp. 5883-5889. https://doi.org/10.18653/v1/D19-1600

[6] Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT:Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., & Solorio, T. (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; June 2-7, 2019; Minneapolis, MN, USA. Volume 1, pp.4171-4186. https://doi.org/10.18653/v1/N19-1423

[7] Eryiğit, G. (2014). ITU Turkish NLP web service. In: Bentivogli, L., Koehn, P., & Manning, C. (Eds.), Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics; April 26-30, 2014, Gothenburg, Sweden. pp. 1-4. Association for Computational Linguistics. https://doi.org/10.3115/v1/E14-2001

[8] García-Haro, J. M., Oña, E. D., Hernandez-Vicen, J., Martinez, S., & Balaguer, C. (2021). Service robots in catering applications: A review and future challenges. Electronics, 10(1), Article 47. https://doi.org/10.3390/electronics10010047

[9] Hayakawa, T., Haddad, W. M., & Hovakimyan, N. (2008). Neural network adaptive control for a class of nonlinear uncertain dynamical systems with asymptotic stability guarantees. IEEE Transactions on Neural Networks, 19(1), 80-89. https://doi.org/10.1109/TNN.2007.902704

[10] Hung, C.-F., Lin, Y., Ciou, H.-J., Wang, W.-Y., & Chiang, H.-H. (2021). Foodtemi: The AI-oriented catering service robot. In: Proceedings of the 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW); September 15-17, 2021; Penghu, Taiwan. pp. 1-2. https://doi.org/10.1109/ICCE-TW52618.2021.9603096

[11] Maj, K., & Zarzycki, P. (2019). Meeting with social robots like the cat-cucumber meeting? An integrated model of humanrobot first contact: Psychological perspective. Paladyn, Journal of Behavioral Robotics, 10(1), 454-465. https://doi.org/10.1515/pjbr-2019-0026

[12] Mukanova, A., Barlybayev, A., Nazyrova, A., Kussepova, L., Matkarimov, B., & Abdikalyk, G. (2024). Development of a geographical question-answering system in the Kazakh language. IEEE Access, 12, 105460-105469. https://doi.org/10.1109/ACCESS.2024.3433426

[13] Nayyar, A., Puri, V., Nguyen, N. G., & Le, D.-N. (2018). Smart surveillance robot for real-time monitoring and control system in environment and industrial applications. In: Bhateja, V., Nguyen, B. L., Nguyen, N. G., Satapathy, S. C., & Le, D.-N. (Eds.), Information systems design and intelligent applications. Berlin: Springer. pp. 229-243. https://doi.org/10.1007/978-981-10-7512-4_23

[14] Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). SQuAD:100,000+ questions for machine comprehension of text. In: Su, J., Carreras, X., & Duh, K. (Eds.), Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing; November 1-4, 2016; Austin, Texas, USA. pp. 2383-2392. https://doi.org/10.18653/v1/D16-1264

[15] Rigatos, G., Abbaszadeh, M., Sari, B., Siano, P., Cuccurullo, G., & Zouari, F. (2023). Nonlinear optimal control for a gas compressor driven by an induction motor. Results in Control and Optimization, 11, 100226. https://doi.org/10.1016/j.rico.2023.100226

[16] Seo, T., Jeon, Y., Park, C., & Kim, J. (2019). Survey on glass and façade-cleaning robots: Climbing mechanisms, cleaning methods, and applications. International Journal of Precision Engineering and Manufacturing-Green Technology, 6(2), 367-376. https://doi.org/10.1007/s40684-019-00079-4

[17] Spitale, M., Silleresi, S., Cosentino, G., Panzeri, F., & Garzotto, F. (2020). “Whom would you like to talk with?”:Exploring conversational agents for children’s linguistic assessment. In: Santos, O. C., Dillenbourg, P., & Börner, K. (Eds.), Proceedings of the Interaction Design and Children Conference; June 21-24, 2020; London, UK. pp. 262-272. https://doi.org/10.1145/3392063.3394421

[18] Toscano, E., Spitale, M., & Garzotto, F. (2022). Socially assistive robots in smart homes: Design factors that influence the user perception. In: Proceedings of the 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI); March 7-10, 2022; Sapporo, Japan. pp. 1075-1079. https://doi.org/10.1109/HRI53351.2022.9889467

[19] van der, Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704. https://doi.org/10.2307/25148660

[20] Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. arXiv. Preprint. https://doi.org/10.48550/arXiv.1706.03762

[21] Zouari, F., Ben Saad, K., & Benrejeb, M. (2013). Adaptive backstepping control for a class of uncertain single input single output nonlinear systems. In: Proceedings of the 10th International Multi-Conference on Systems, Signals & Devices (SSD); May 6-10, 2022; Setif, Algeria. pp. 1-6. https://doi.org/10.1109/SSD.2013.6564134