  This paper proposes a Social Reward Sources (SRS) design for a Human-Robot
Collaborative Navigation (HRCN) task: human-robot collaborative search. It is a
flexible approach capable of handling the collaborative task, human-robot
interaction and environment restrictions, all integrated on a common
environment. We modelled task rewards based on unexplored area observability
and isolation and evaluated the model through different levels of human-robot
communication. The models are validated through quantitative evaluation against
both agents' individual performance and qualitative surveying of participants'
perception. After that, the three proposed communication levels are compared
against each other using the previous metrics.
