Tongfei Bian

Tongfei Bian, Mathieu Chollet, Tanaya Guha

For a robot to be called socially intelligent, it must be able to infer users internal states from their current behaviour, predict the users future behaviour, and if required, respond appropriately. In this work, we investigate how robots can be endowed with such social intelligence by modelling the dynamic relationship between user's internal states (latent) and actions (observable state). Our premise is that these states arise from the same underlying socio-cognitive process and influence each other dynamically. Drawing inspiration from theories in Cognitive Science, we propose a novel multi-task learning framework, termed as \textbf{SocialLDG} that explicitly models the dynamic relationship among the states represent as six distinct tasks. Our framework uses a language model to introduce lexical priors for each task and employs dynamic graph learning to model task affinity evolving with time. SocialLDG has three advantages: First, it achieves state-of-the-art performance on two challenging human-robot social interaction datasets available publicly. Second, it supports strong task scalability by learning new tasks seamlessly without catastrophic forgetting. Finally, benefiting from explicit modelling task affinity, it offers insights on how different interactions unfolds in time and how the internal states and observable actions influence each other in human decision making.

Tongfei Bian, Yiming Ma, Mathieu Chollet et al.

For efficient human-agent interaction, an agent should proactively recognize their target user and prepare for upcoming interactions. We formulate this challenging problem as the novel task of jointly forecasting a person's intent to interact with the agent, their attitude towards the agent and the action they will perform, from the agent's (egocentric) perspective. So we propose \emph{SocialEgoNet} - a graph-based spatiotemporal framework that exploits task dependencies through a hierarchical multitask learning approach. SocialEgoNet uses whole-body skeletons (keypoints from face, hands and body) extracted from only 1 second of video input for high inference speed. For evaluation, we augment an existing egocentric human-agent interaction dataset with new class labels and bounding box annotations. Extensive experiments on this augmented dataset, named JPL-Social, demonstrate \emph{real-time} inference and superior performance (average accuracy across all tasks: 83.15\%) of our model outperforming several competitive baselines. The additional annotations and code will be available upon acceptance.