Advancing Human-Robot Interaction: The Role of Natural Language Processing in Robotic Systems
- DOI
- 10.2991/978-94-6463-512-6_23How to use a DOI?
- Keywords
- Natural Language Processing; Robotic Communication; Adaptive Learning Systems
- Abstract
As robotics and artificial intelligence continue to evolve, the integration of Natural Language Processing (NLP) has become a pivotal element in enhancing robotic functionalities. This paper explores how NLP facilitates advanced interactions between humans and robots, emphasizing technological advancements and applications that bolster machine understanding and interaction capabilities. It discusses the progression from basic NLP implementations in robots to sophisticated systems capable of complex communication and contextual adaptation. Challenges such as linguistic ambiguities, multilingual processing, and the ethical implications of robotic interactions are also examined. The conclusion posits future developments in NLP that promise more intuitive and adaptive robotic functionalities, advocating for continuous innovation and interdisciplinary research to overcome existing limitations and fully harness NLP’s potential in robotics.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xiao Wang PY - 2024 DA - 2024/09/23 TI - Advancing Human-Robot Interaction: The Role of Natural Language Processing in Robotic Systems BT - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024) PB - Atlantis Press SP - 195 EP - 209 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-512-6_23 DO - 10.2991/978-94-6463-512-6_23 ID - Wang2024 ER -