Mood Dynamics: Integrating Emotional Expressiveness into Robotic Task Execution

This paper introduces Mood Dynamics, a framework for generating emotive emergent behaviors in robotic task completion by extending Laban's unpublished model of Effort-Shape with modern computational tools. The framework integrates a dynamic Line of Axis <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L(t)$</tex>, which ensures stability and alignment, with Effort <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E(t)$</tex>, capturing the emotive and behavioral qualities of movement. By implementing a GAN-RL architecture and probabilistic sampling, Mood Dynamics enables robots to adapt their trajectories in real-time, conveying emotion while executing tasks. The core rules of Mood Dynamics, including Inclination and Attunement, are defined as computational formulations, providing a foundation for future research in task-oriented expressive robotic systems.

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Publication Details

Published
2025-03-04
DOI
https://doi.org/10.1109/hri61500.2025.10973860
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
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article

Mood Dynamics: Integrating Emotional Expressiveness into Robotic Task Execution

N Miya Sylvester
Reinforcement Learning in Robotics
article

Mood Dynamics: Integrating Emotional Expressiveness into Robotic Task Execution

N Miya Sylvester
article en

Abstract

This paper introduces Mood Dynamics, a framework for generating emotive emergent behaviors in robotic task completion by extending Laban's unpublished model of Effort-Shape with modern computational tools. The framework integrates a dynamic Line of Axis <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L(t)$</tex>, which ensures stability and alignment, with Effort <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E(t)$</tex>, capturing the emotive and behavioral qualities of movement. By implementing a GAN-RL architecture and probabilistic sampling, Mood Dynamics enables robots to adapt their trajectories in real-time, conveying emotion while executing tasks. The core rules of Mood Dynamics, including Inclination and Attunement, are defined as computational formulations, providing a foundation for future research in task-oriented expressive robotic systems.

Carnegie Mellon University (US)
Openalex Percentile: Top 95%
Reinforcement Learning in Robotics
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