  Understanding human social behaviour is crucial in computer vision and
robotics. Micro-level observations like individual actions fall short,
necessitating a comprehensive approach that considers individual behaviour,
intra-group dynamics, and social group levels for a thorough understanding. To
address dataset limitations, this paper introduces JRDB-Social, an extension of
JRDB. Designed to fill gaps in human understanding across diverse indoor and
outdoor social contexts, JRDB-Social provides annotations at three levels:
individual attributes, intra-group interactions, and social group context. This
dataset aims to enhance our grasp of human social dynamics for robotic
applications. Utilizing the recent cutting-edge multi-modal large language
models, we evaluated our benchmark to explore their capacity to decipher social
human behaviour.
