Perceiving the person and their interactions with the others for social robotics – A review

Social robots need to understand human activities, dynamics, and the intentions behind their behaviors. Most of the time, this implies the modeling of the whole scene. The recognition of the activities and intentions of a person are inferred from the perception of the individual, but also from their interactions with the rest of the environment (i.e., objects and/or people). Centering on the social nature of the person, robots need to understand human social cues, which include verbal but also nonverbal behavioral signals such as actions, gestures, body postures, facial emotions, and proxemics. The correct understanding of these signals helps these robots to anticipate the needs and expectations of people. It also avoids abrupt changes on the human–robot interaction, as the temporal dynamics of interactions are anchored and driven by a major repertoire of social landmarks. Within the general framework of interaction of robots with their human counterparts, this paper reviews recent approaches for recognizing human activities, but also for perceiving social signals emanated from a person or a group of people during an interaction. The perception of visual and/or audio signals allow them to correctly localize themselves with respect to humans from the environment while also navigating and/or interacting with a person or a group of people.