Affective Computing in Human–Robot Interaction

Dear Colleagues,

We are pleased to announce a call for papers for this Special Issue titled “Affective Computing in Human–Robot Interaction”. This collection aims to explore the latest advances in affective intelligence technologies that enable robots to better perceive, understand, express, and respond to human emotional and affective states. We warmly invite contributions from academia and industry to foster progress in this rapidly evolving and interdisciplinary domain.

Scope, Focus, and Purpose of the Topical Collection

1. Focus

This collection focuses on real-world interaction scenarios in which robots are equipped with core affective capabilities, including emotion perception, emotion understanding, emotion-driven decision-making, and affective expression.

2. Scope

We welcome research on cutting-edge technologies and applications such as multimodal affective computing models, emotional learning and reasoning, emotion-aware human–robot interaction in the era of large models, and practical affective robotic systems.

3. Purpose

The Special Issue aims to achieve the following:

• Advance a systematic research framework for affective interaction;
• Accelerate the deployment of affective algorithms across diverse real-world scenarios;
• Enhance the trustworthiness, naturalness, and adaptivity of social robots in human–robot interaction.

Relationship to and Contribution Beyond Existing Literature

While existing affective computing research often concentrates on isolated emotional capacities (e.g., recognition or expression), and robotics research typically emphasizes task execution or control, this Special Issue will close these gaps by achieving the following:

• Establishing a full-chain perspective of affective interaction: from emotion perception → understanding → decision-making and expression → social context adaptation;
• Leveraging multimodal signals and reasoning capabilities of large models to improve social intelligence;
• Emphasizing contextualized and long-term interaction research, moving beyond laboratory settings;
• Extending evaluation methodologies and ethical considerations to promote responsible emotional interaction technologies.

Overall, this collection provides a more unified, comprehensive, and forward-looking perspective for affective robotics research.

Topics of Interest

As robots are increasingly deployed in social assistance and collaborative environments, recognizing and responding to human emotions has become essential to fostering trust, empathy, and natural interaction. This Special Issue covers a wide range of topics related to affective computing in human–robot interaction, including, but not limited to, the following:

1. Emotion perception and affective sensing: Multimodal emotion recognition for interaction scenarios, including facial expressions, speech prosody, body movements, gaze behavior, physiological signals, and contextual cues.
2. Emotion modeling and understanding: Computational models of emotional states, cognitive appraisal, emotional contagion, personality traits, and social context.
3. Emotion-driven behavior generation: Decision-making and strategy generation techniques enabling robots to respond appropriately and adaptively to human emotions.
4. Affective expression in robots: Multimodal mechanisms for emotional expression, such as vocal, facial, gestural, and tactile communication.
5. Large-model techniques for affective interaction: Using large language models to enhance affective reasoning, social dialogue, and natural human–robot communication.
6. Affective social learning and adaptation: Reinforcement learning, imitation learning, and continual learning for personalized and long-term affective interaction.
7. Evaluation of affective interaction systems: Metrics, datasets, and experimental protocols for assessing affective interaction quality and robustness.
8. Ethical, societal, and privacy considerations: Ethical principles, emotional safety, fairness, data privacy, and long-term societal impacts of affective robots.

We look forward to hearing from you.

Sincerely,

Prof. Dr. Tao Wang
Dr. Yunjia Sun
Guest Editors

Manuscript Submission Information

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