Natural Language Processing for Human–Robot Interaction: A Comprehensive Review of Methods, Challenges, and Future Directions

Abstract

Natural Language Processing (NLP) has been established as a foundational technology for intelligent and socially responsible Human-Robot Interaction (HRI). The technology helps robots effectively, meaningfully, and naturally process and react to human communication. A critical analysis of a compilation of landmark publications from 2020 to 2025 has been presented, focusing on the major technological advancements in the development or implementation of HRI technology based on Natural Language Processing. The results achieved a shift towards a data-intensive, data-driven, or even transformation-based approach to symbolic or probabilistic frameworks for language, leading to improvements in the accuracy of interpreting, accomplishing, or detecting contextual meaning and emotions. Despite this, a plethora of challenges remain to be addressed, including attaining or sustaining real-time responses, handling domain data gaps effectively, adapting to multilingual frameworks, and embracing approaches to ethics & bias, among others. The current trends continue to encompass developments in hybrid architectures, low-energy-consumption approaches suitable for embedded applications, culturally adapted dialogical frameworks, and ethically anchored communication schemes, among others.