Optimizing Robotic Interaction and Decision Making Using Advanced NLP and AI Technologies

Abstract

Background: Incorporating Natural Language Processing (NLP) and Artificial Intelligence (AI) algorithms into robotic assistants can further improve their capacity to independently execute more sophisticated tasks across various sectors, such as healthcare, manufacturing, or retail. However, there have been numerous roadblocks to making them practical from an end-user perspective and addressing ethical issues.Objective: The article delves into the role of NLP and AI assimilation in extending the functional capabilities of robotic assistants, specifically improving language comprehension, decision-making accuracy, and adaptive learning. Additionally, explores the ethical consequences of increased robot independence.Methods: The methodology uses two publicly available datasets that facilitated the training and testing of these language models: 1) Multi-Domain Dialogue Dataset (MDDD), which has half a million labeled conversations with tasks as complex as open-domain multi-turn dialogues, and 2) OpenAI Language Interaction Data set (OLID), an interaction data collected from over one million human-robot interactions. The robots’ performance was tested in simulated and real-world settings, including healthcare, manufacturing, and retail.Results: Healthcare exhibited an increase in language comprehension accuracy of 16% (84% post-integration), while autonomous decision-making and manufacturing improved by around 25% (83% post-integration), as well as customer services response rate and retail reaching around upto15% (80% post-integration) improvements. Adaptive learning effectiveness improved by 27% in manufacturing, indicating the robots obtained better performance as they worked.Conclusion: Combining NLP with AI allows robotic assistants to do more complicated, contextually aware activities, expanding their usefulness across many disciplines. While acknowledging the many advances, continual study and investigation are essential to navigate robotic helper technology and its many uses. Though these advancements show potential, upcoming studies need to concentrate on reducing ethical risks such as bias and privacy issues to guarantee the responsible implementation of AI-powered robots.