Gestures play a crucial role in human communication, enhancing interpersonal interactions through non-verbal expression. Burgeoning technology allows virtual avatars to leverage communicative gestures to enhance their life-likeness and communication quality with AI-generated gestures. Traditionally, evaluations of AI-generated gestures have been confined to 2D settings. However, Virtual Reality (VR) offers an immersive alternative with the potential to affect the perception of virtual gestures. This paper introduces a novel evaluation approach for computer-generated gestures, investigating the impact of a fully immersive environment compared to a traditional 2D setting. The goal is to find the differences, benefits, and drawbacks of the two alternatives. Furthermore, the study also aims to investigate three gesture generation algorithms submitted to the 2023 GENEA Challenge and evaluate their performance in the two virtual settings. Experiments showed that the VR setting has an impact on the rating of generated gestures. Participants tended to rate gestures observed in VR slightly higher on average than in 2D. Furthermore, the results of the study showed that the generation models used for the study had a consistent ranking. However, the setting had a limited impact on the models' performance, having a bigger impact on the perception of 'true movement' which had higher ratings in VR than in 2D.

This paper focuses on enhancing human-agent communication by integrating spatial context into virtual agents’ non-verbal behaviors, specifically gestures. Recent advances in co-speech gesture generation have primarily utilized data-driven methods, which create natural motion but limit the scope of gestures to those performed in a void. Our work aims to extend these methods by enabling generative models to incorporate scene information into speech-driven gesture synthesis. We introduce a novel synthetic gesture dataset tailored for this purpose. This development represents a critical step toward creating embodied conversational agents that interact more naturally with their environment and users.

This paper describes a system developed for the GENEA (Generation and Evaluation of Non-verbal Behaviour for Embodied Agents) Challenge 2023. Our solution builds on an existing difusion-based motion synthesis model. We propose a contrastive speech and motion pretraining (CSMP) module, which learns a joint embedding for speech and gesture with the aim to learn a semantic coupling between these modalities. The output of the CSMP module is used as a conditioning signal in the difusion-based gesture synthesis model in order to achieve semantically-aware co-speech gesture generation. Our entry achieved highest human-likeness and highest speech appropriateness rating among the submitted entries. This indicates that our system is a promising approach to achieve human-like co-speech gestures in agents that carry semantic meaning.

One of the main goals of robotics and intelligent agent research is to enable them to communicate with humans in physically situated settings. Human communication consists of both verbal and non-verbal modes. Recent studies in enabling communication for intelligent agents have focused on verbal modes, i.e., language and speech. However, in a situated setting the non-verbal mode is crucial for an agent to adapt flexible communication strategies. In this work, we focus on learning to generate non-verbal communicative expressions in situated embodied interactive agents. Specifically, we show that an agent can learn pointing gestures in a physically simulated environment through a combination of imitation and reinforcement learning that achieves high motion naturalness and high referential accuracy. We compared our proposed system against several baselines in both subjective and objective evaluations. The subjective evaluation is done in a virtual reality setting where an embodied referential game is played between the user and the agent in a shared 3D space, a setup that fully assesses the communicative capabilities of the generated gestures. The evaluations show that our model achieves a higher level of referential accuracy and motion naturalness compared to a state-of-the-art supervised learning motion synthesis model, showing the promise of our proposed system that combines imitation and reinforcement learning for generating communicative gestures. Additionally, our system is robust in a physically-simulated environment thus has the potential of being applied to robots.

Emotional expressivity is essential for successful Human-Robot Interaction. However, robots often have different levels of expressivity in their face and voice. Here we ask whether this modality mismatch influences human behaviour and perception of the robot. Participants played a cooperative task with a robot that displayed matched and mismatched smiling expressions in the face and voice. Emotional expressivity did not influence acceptance of robot's recommendations or subjective evaluations of the robot. However, we found that the robot had overall a higher social influence than a virtual character, and was evaluated more positively.

Pointing is an important mode of interaction with robots. While large amounts of prior studies focus on recognition of human pointing, there is a lack of investigation into generating context-aware human-like pointing gestures, a shortcoming we hope to address. We first collect a rich dataset of human pointing gestures and corresponding pointing target locations with accurate motion capture. Analysis of the dataset shows that it contains various pointing styles, handedness, and well-distributed target positions in surrounding 3D space in both single-target pointing scenario and two-target point-and-place.We then train reinforcement learning (RL) control policies in physically realistic simulation to imitate the pointing motion in the dataset while maximizing pointing precision reward.We show that our RL motion imitation setup allows models to learn human-like pointing dynamics while maximizing task reward (pointing precision). This is promising for incorporating additional context in the form of task reward to enable flexible context-aware pointing behaviors in a physically realistic environment while retaining human-likeness in pointing motion dynamics.

In this paper we present a pilot study which investigates how non-verbal behavior affects social influence in social robots. We also present a modular system which is capable of controlling the non-verbal behavior based on the interlocutor's facial gestures (head movements and facial expressions) in real time, and a study investigating whether three different strategies for facial gestures ("still", "natural movement", i.e. movements recorded from another conversation, and "copy", i.e. mimicking the user with a four second delay) has any affect on social influence and decision making in a "survival task". Our preliminary results show there was no significant difference between the three conditions, but this might be due to among other things a low number of study participants (12). 

When we practice a movement, human brains creates a motor memory of it. These memories are formed and stored in the brain as representations which allows us to perform familiar tasks faster than new movements. From a developmental robotics and embodied artificial agent perspective it could be also beneficial to exploit the concept of these motor representations in the form of spatial-temporal motion priors for complex, full-body motion synthesis. Encoding such priors in neural networks in a form of inductive biases inherit essential spatio-temporality aspect of human motion. In our current work we examine and compare recent approaches for capturing spatial and temporal dependencies with machine learning algorithms that are used to model human motion.