Humans naturally produce nonverbal signals such as facial expressions, body movements, hand gestures, and tone of voice, along with words, to communicate their messages, opinions, and feelings. Considering robots are progressively moving out from research laboratories into human environ- ments, it is increasingly desirable that they develop a similar social intelligence. Therefore, equipping social robots with non- verbal communication skills has been an active research area for decades, where data-driven, end-to-end learning approaches have become predominant in recent years, offering scalabil- ity and generalisability. However, most of these approaches consider a single character, modelling intrapersonal dynamics only. In this paper, we propose a method based on condi- tional Generative Adversarial Networks, intending to generate behaviours for a robot in affective dyadic interactions. Our method takes as an input the audio of a target person together with the nonverbal signals of their interacting partner, modelled by a novel Context Encoder, to generate appropriate body gestures. We evaluate our method on the multimodal JESTKOD dataset that comprises dyadic interactions under agreement and disagreement scenarios. The experimental results show that Context Encoder can better contribute to the prediction of co- speech gestures in agreement situations.

Agree or Disagree? Generating Body Gestures from Affective Contextual Cues during Dyadic Interactions

**Session Chairs: Wallace Lawson, Fumihide Tanaka**

Long Session III: Non-verbal Cues and Expressiveness

technical paper

**31st IEEE International Conference on Robot & Human Interactive Communication**
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 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
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 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
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IEEE RO-MAN 2022

31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

Tuyen Nguyen

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Agree or Disagree? Generating Body Gestures from Affective Contextual Cues during Dyadic Interactions

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