Exploiting a Human-Aware World Model for Dynamic Task Allocation in Flexible Human-Robot Teams

We propose a highly flexible approach to human-robot cooperation, where a robot dynamically selects operations contributing to a shared goal from a given task model. Therefore, knowledge on the task progress is extracted from a world model constructed from eye-in-hand camera images. Data generated from such partial workspace observations is not reliable over time, as humans may interact with resources. We therefore use a human-aware world model maintaining a measure for trust in stored objects regarding recent human presence and previous task progress. Our contribution is an action selection algorithm that uses this trust measure to interleave task operations with active vision to refresh the world model. Large-scale experiments cover various sorts of human participation in different benchmark tasks through simulation of simplified, partially randomized human models. Results illuminate system behaviour and performance for different parametrizations of our human-robot teaming framework.