Human-robot Collaborative Navigation Search using Social Reward Sources
This work addresses collaborative search tasks for human-robot teams, but it appears incremental as it builds on existing navigation and interaction methods.
The paper tackled human-robot collaborative navigation search by proposing a Social Reward Sources design, which integrates task rewards based on unexplored area observability and isolation, and evaluated it through quantitative performance metrics and qualitative surveys across different communication levels.
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.