Communication Strategy for Efficient Guidance Providing : Domain-structure Awareness, Performance Trade-offs, and Value of Future Observations

Service robots are gaining capabilities to be deployed in public environments for human assistance. While robot actively providing guidance has shown great success in field study, the communication strategy (the strategy to decide whom to initiate the service for and when), and hence the performance evaluation, has been based on behavioral-based qualitative analysis. We attribute this to the challenge of accessing large-scale field data with condition control, and approach the problem with simulation from the agent-based modeling literature, to simulate pedestrian behavior in unfamiliar environments and estimate travel cost. We contribute a planning approach that uses the pedestrian behavior prediction from the model, to decide whom to initiate guidance and when for performance maximization. The results suggest that our approach is more efficient based on the measure of saved pedestrian travel time, compared to the behavioral-based strategy and a baseline that maximizes service counts.