Nguyen Tan Viet Tuyen

Truong Le Minh Toan, Dieu Bang Mach, Tan Duy Le et al.

Mental health challenges are rising globally, while traditional support services face limited availability and high costs. Large language models offer potential for conversational support, but often lack personalization, empathy, and factual grounding. A virtual agent framework is introduced to provide empathetic, personalized, and reliable wellbeing support through retrieval-augmented architecture, structured memory, and multimodal interaction. Objective benchmarks demonstrate improved retrieval and response quality, particularly for smaller models. A cross-cultural study with university students from Vietnam and Australia shows the system outperforms LLM-only baselines in coherence, perceived accuracy, and empathy, with most participants clearly preferring the proposed approach.

Nguyen Tan Viet Tuyen, Oya Celiktutan

We are approaching a future where social robots will progressively become widespread in many aspects of our daily lives, including education, healthcare, work, and personal use. All of such practical applications require that humans and robots collaborate in human environments, where social interaction is unavoidable. Along with verbal communication, successful social interaction is closely coupled with the interplay between nonverbal perception and action mechanisms, such as observation of gaze behaviour and following their attention, coordinating the form and function of hand gestures. Humans perform nonverbal communication in an instinctive and adaptive manner, with no effort. For robots to be successful in our social landscape, they should therefore engage in social interactions in a humanlike way, with increasing levels of autonomy. In particular, nonverbal gestures are expected to endow social robots with the capability of emphasizing their speech, or showing their intentions. Motivated by this, our research sheds a light on modeling human behaviors in social interactions, specifically, forecasting human nonverbal social signals during dyadic interactions, with an overarching goal of developing robotic interfaces that can learn to imitate human dyadic interactions. Such an approach will ensure the messages encoded in the robot gestures could be perceived by interacting partners in a facile and transparent manner, which could help improve the interacting partner perception and makes the social interaction outcomes enhanced.