Pose Generation for Social Robots in Conversational Group Formations.

ABSTRACT: We study two approaches for predicting an appropriate pose for a robot to take part in group formations typical of social human conversations subject to the physical layout of the surrounding environment. One method is model-based and explicitly encodes key geometric aspects of conversational formations. The other method is data-driven. It implicitly models key properties of spatial arrangements using graph neural networks and an adversarial training regimen. We evaluate the proposed approaches through quantitative metrics designed for this problem domain and via a human experiment. Our results suggest that the proposed methods are effective at reasoning about the environment layout and conversational group formations. They can also be used repeatedly to simulate conversational spatial arr

SUBMITTER: Vazquez M 

PROVIDER: S-EPMC8801779 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature