Bridging the Gap Between Natural Language Processing (NLP) and Robotics for Human-Robot Interaction

EasyChair Preprint 13894

Abstract

This research addresses the challenge of enabling robots to understand and respond to natural language instructions and human emotions, aiming to facilitate more natural and intuitive human-robot interaction. By integrating advanced natural language processing (NLP) techniques with robotic control systems, the study explores ways to enhance the communication and collaboration between humans and robots. Key areas of focus include developing algorithms for real-time language comprehension, emotional recognition, and context-aware responses. The integration of NLP with robotics aims to create robots that can interpret complex instructions, understand nuances in human speech, and respond appropriately to emotional cues. This research has significant implications for improving the usability and effectiveness of robots in various applications, such as healthcare, customer service, and assistive technologies. By bridging the gap between NLP and robotics, the study seeks to advance the field of human-robot interaction, making robots more adaptable, empathetic, and user-friendly.

Keyphrases: Human-Robot Collaboration, Intuitive Interaction, NLP, Natural Language Processing, Robotics, assistive technologies, context-aware responses, emotional recognition, human-robot interaction, language comprehension, real-time language processing

@booklet{EasyChair:13894,
  author    = {Dylan Stilinki and Saleh Mohamed},
  title     = {Bridging the Gap Between Natural Language Processing (NLP) and Robotics for Human-Robot Interaction},
  howpublished = {EasyChair Preprint 13894},
  year      = {EasyChair, 2024}}