A Multi-Behavior Planning Framework for Robot Guide
This work addresses the need for more proactive and communicative robot guides for users in navigation tasks, though it appears incremental by building on existing socially-aware navigation methods.
The paper tackled the problem of robot guiding by addressing the lack of interactive behavior planning in existing models, proposing a multi-behavior planning framework based on Monte Carlo Tree Search that improved assistance in understanding scenes and path selection, with validation in simulation and real-world experiments.
The guiding task of a mobile robot requires not only human-aware navigation, but also appropriate yet timely interaction for active instruction. State-of-the-art tour-guide models limit their socially-aware consideration to adapting to users' motion, ignoring the interactive behavior planning to fulfill the communicative demands. We propose a multi-behavior planning framework based on Monte Carlo Tree Search to better assist users to understand confusing scene contexts, select proper paths and timely arrive at the destination. To provide proactive guidance, we construct a sampling-based probability model of human motion to consider the interrelated effects between robots and humans. We validate our method both in simulation and real-world experiments along with performance comparison with state-of-the-art models.