In recent years, robotic applications have seen an increasing real-world deployment. It is common in these applications that a user interacts directly with a robot. In such Human-Robot Interaction (HRI), realistic and appropriate robot behavioural reaction plays a key role in realistic interactions, with non-verbal reactions providing important cues for expressing robots’ intentions and emotions. In real-world human-human interactions, a broad spectrum of reactions might be appropriate (plausible) for human listeners in response to a specific speaker behaviour due to various person-specific factors (e.g., personality, emotional states). As a result, the capability of the robot to generate realistic, diverse and appropriate human-style verbal and nonverbal reactions according to the subject it interacts with is crucial.

Following the success in 2022 and 2023, the third AHRI workshop will continue to provide a communication and collaboration platform for researchers working on affective computing, generative AI, HRI, social robotics, and AI and robotics application. In alignment with the ACII 2024’s theme in “Human and beyond”, we especially welcome submissions on HRI in generative AI and naturalistic interaction contexts. This workshop will focus on discussing the following topics:

1. How to adaptively/accurately perceive unimodal or multimodal affective human behaviour in HRI under particular interaction context.

2. How to efficiently generate natural and affective robot behaviour in HRI that is appropriate for the interaction context.

3. How to measure the benefits and outcomes of affective HRI applications with a user-centred and contextualised approach.

4. The underlying mechanism of deep learning models in generating reactions to perceived human behaviours.

References:

[1] Song, Siyang, Micol Spitale, Cheng Luo, Germán Barquero, Cristina Palmero, Sergio Escalera, Michel Valstar et al. "React2023: The first multiple appropriate facial reaction generation challenge." In Proceedings of the 31st ACM International Conference on Multimedia, pp. 9620-9624. 2023.

[2] Song, Siyang, Zilong Shao, Shashank Jaiswal, Linlin Shen, Michel Valstar, and Hatice Gunes. "Learning person-specific cognition from facial reactions for automatic personality recognition." IEEE Transactions on Affective Computing (2022).

Our workshop will bring together researchers from the disciplines of HRI, affective computing, healthcare, cognitive sc and various related fields to facilitate discussion and future collaborations. We expect that this will greatly advance the benefits of affective computing systems for healthcare applications and affective HRI research.