Sentiment-based Engagement Strategies for intuitive Human-Robot Interaction
This work addresses the problem of improving mutual understanding and reducing misjudgments in human-robot interactions for robotics and AI applications, but it appears incremental as it builds on existing sentiment analysis and engagement techniques.
The paper tackled the challenge of intuitive human-robot interaction by detecting human emotional states and attention to select engagement strategies, implementing four strategies on a mobile robot platform for initial experiments.
Emotion expressions serve as important communicative signals and are crucial cues in intuitive interactions between humans. Hence, it is essential to include these fundamentals in robotic behavior strategies when interacting with humans to promote mutual understanding and to reduce misjudgements. We tackle this challenge by detecting and using the emotional state and attention for a sentiment analysis of potential human interaction partners to select well-adjusted engagement strategies. This way, we pave the way for more intuitive human-robot interactions, as the robot's action conforms to the person's mood and expectation. We propose four different engagement strategies with implicit and explicit communication techniques that we implement on a mobile robot platform for initial experiments.