Natural language generation for social robotics: opportunities and challenges

Area of Science:

• Social Robotics
• Computational Linguistics
• Human-Robot Interaction

Background:

• Social robots aim for human-like interaction, but often use simplistic language generation.
• Face-to-face dialogue relies on rich verbal and non-verbal cues, which current robots struggle to replicate.
• Natural Language Generation (NLG) focuses on automated text production, primarily for written content.

Purpose of the Study:

• To review the current state of social robotics and Natural Language Generation (NLG).
• To identify reasons for the limited use of advanced NLG in social robots.
• To propose methods for integrating NLG techniques into social robotics for enhanced interaction.

Main Methods:

• Literature review of social robotics and NLG research.
• Analysis of current limitations in social robot language capabilities.
• Proposal of an approach for applying NLG to social robotics.

Main Results:

• Most social robots utilize basic language generation, not sophisticated NLG.
• There's a gap between NLG advancements and their application in social robotics.
• Specific NLG techniques can be adapted for socially interactive robots.

Conclusions:

• Integrating advanced NLG is crucial for developing more socially intelligent robots.
• Bridging the gap between NLG and social robotics can significantly improve human-robot communication.
• Future research should focus on adapting NLG tools for real-time, interactive robot dialogue.