Pieter Wolfert

This full-day workshop focuses on advancing research and development in socially-competent agents that care. Many agent platforms and kinds of embodiments of agents that have a primary focus on caring, one way or another, for the end users. In this workshop, we aim to look at value-based design, inclusiveness, empathetic design and long-term human-agent interaction for socially competent agents that care.

Embodied conversational agents that interact socially with people in the physical world require multi-modal capabilities, such as appropriately responding to visual features of users. While existing vision-and-language models can generate language based on visual input, this language is not situated in a social interaction in the physical world. We present a novel task called Visual Conversation Starters, where an agent generates a conversation-starting question referring to features visible in an image of the user. We collect a dataset of 4,000 images of people with 12,000 crowdsourced conversation starters, and compare various model architectures: fine-tuning smaller seq2seq or image-to-text models versus zero-shot prompting of GPT-3.5, using image captions versus end-to-end image input, and training on human data versus synthetic questions generated by GPT-3.5. Models were used to generate friendly conversation starters which were evaluated on criteria including language fluency, visual grounding, interestingness and politeness. Results show that GPT-3.5 generates more interesting and polite questions than smaller models that are fine-tuned on crowdsourced data, but vision-to-language models are better at referencing visual features, and they can mimick GPT-3.5's performance. This demonstrates the feasibility of deep visiolinguistic models for situated social agents, forming an important first stage in creating situated multimodal social interaction.

This article reports on the second GENEA Challenge to benchmark data-driven automatic co-speech gesture generation. Participating teams used the same speech and motion dataset to build gesture-generation systems. Motion generated by all these systems was rendered to video using a standardised visualisation pipeline and evaluated in several large, crowdsourced user studies. Unlike when comparing different research articles, differences in results are here only due to differences between methods, enabling direct comparison between systems. The dataset was based on 18 hours of full-body motion capture, including fingers, of different persons engaging in a dyadic conversation. Ten teams participated in the challenge across two tiers: full-body and upper-body gesticulation. For each tier, we evaluated both the human-likeness of the gesture motion and its appropriateness for the specific speech signal. Our evaluations decouple human-likeness from gesture appropriateness, which has been a difficult problem in the field. The evaluation results show some synthetic gesture conditions being rated as significantly more human-like than 3D human motion capture. To the best of our knowledge, this has not been demonstrated before. On the other hand, all synthetic motion is found to be vastly less appropriate for the speech than the original motion-capture recordings. We also find that conventional objective metrics do not correlate well with subjective human-likeness ratings in this large evaluation. The one exception is the Fréchet gesture distance (FGD), which achieves a Kendall’s tau rank correlation of around -0.5. Based on the challenge results we formulate numerous recommendations for system building and evaluation.

Previous work has observed how Neurodivergence is often harmfully pathologized in Human-Computer Interaction (HCI) and Human-Robot interaction (HRI) research. We conduct a review of autism robot reviews and find the dominant research direction is Autistic people's second to lowest (24 of 25) research priority: interventions and treatments purporting to 'help' neurodivergent individuals to conform to neurotypical social norms, become better behaved, improve social and emotional skills, and otherwise 'fix' us -- rarely prioritizing the internal experiences that might lead to such differences. Furthermore, a growing body of evidence indicates many of the most popular current approaches risk inflicting lasting trauma and damage on Autistic people. We draw on the principles and findings of the latest Autism research, Feminist HRI, and Robotics to imagine a role reversal, analyze the implications, then conclude with actionable guidance on Autistic-led scientific methods and research directions.

This paper compares three methods for evaluating computer-generated motion behaviour for animated characters: two commonly used direct rating methods and a newly designed questionnaire. The questionnaire is specifically designed to measure the human-likeness, appropriateness, and intelligibility of the generated motion. Furthermore, this study investigates the suitability of these evaluation tools for assessing subtle forms of human behaviour, such as the subdued motion cues shown when listening to someone. This paper reports six user studies, namely studies that directly rate the appropriateness and human-likeness of a computer character’s motion, along with studies that instead rely on a questionnaire to measure the quality of the motion. As test data, we used the motion generated by two generative models and recorded human gestures, which served as a gold standard. Our findings indicate that when evaluating gesturing motion, the direct rating of human-likeness and appropriateness is to be preferred over a questionnaire. However, when assessing the subtle motion of a computer character, even the direct rating method yields less conclusive results. Despite demonstrating high internal consistency, our questionnaire proves to be less sensitive than directly rating the quality of the motion. The results provide insights into the evaluation of human motion behaviour and highlight the complexities involved in capturing subtle nuances in nonverbal communication. These findings have implications for the development and improvement of motion generation models and can guide researchers in selecting appropriate evaluation methodologies for specific aspects of human behaviour.

Non-verbal behavior is advantageous for embodied agents when interacting with humans. Despite many years of research on the generation of non-verbal behavior, there is no established benchmarking practice in the field. Most researchers do not compare their results to prior work, and if they do, they often do so in a manner that is not compatible with other approaches. The GENEA Workshop 2023 seeks to bring the community together to discuss the major challenges and solutions, and to identify the best ways to progress the field.

This paper asks if recent models for generating co-speech gesticulation also may learn to exhibit listening behaviour as well. We consider two models from recent gesture-generation challenges and train them on a dataset of audio and 3D motion capture from dyadic conversations. One model is driven by information from both sides of the conversation, whereas the other only uses the character’s own speech. Several user studies are performed to assess the motion generated when the character is speaking actively, versus when the character is the listener in the conversation. We find that participants are reliably able to discern motion associated with listening, whether from motion capture or generated by the models. Both models are thus able to produce distinctive listening behaviour, even though only one model is truly a listener, in the sense that it has access to information from the other party in the conversation. Additional experiments on both natural and model-generated motion finds motion associated with listening to be rated as less human-like than motion associated with active speaking.

Children encounter high levels of stress and anxiety before receiving medical treatment, such as a vaccination. This paper explores the effect of emotional gesturing in socially assistive robots (SARs) on children's observed and self-reported engagement, as well as self-reported anxiety, fear, and trust during a group vaccination. A total of 249 children interacted with the social robot iPal before and after receiving the vaccine. Our results show an overall positive effect of adding emotional gestures to a SAR's interaction behavior leading to increased engagement and lower anxiety, while increased engagement also resulted in trusting the robot more. Thus, adding emotional gestures during child-robot interaction is a powerful way to improve the child's experience during a group vaccination day.