Trust-Aware Human–Robot Fusion Decision-Making for Emergency Indoor Patrolling

Trust plays a crucial role in decision-making during human-robot collaboration, particularly in emergency scenarios where it becomes more susceptible due to dynamic factors. Misalignment of human-robot trust significantly hampers the efficiency of the collaboration. Therefore, it is imperative to establish effective human-robot interaction and decision support mechanisms that mitigate biases in human confidence levels regarding the robot’s capabilities in dynamic environments. Additionally, online correction of human-robot trust based on human behavioral feedback is vital. This paper focuses on a specific type of emergency task scenario, specifically, indoor human-robot collaborative patrolling in the event of sudden power outages. We propose a trust model based on linear Gaussian and sparse Gaussian processes (sparse GP). We also employ Monte Carlo Tree Search (MCTS) method to determine the robot’s optimal fusion decision-making. Through VR-based human-robot collaborative experiments, we ascertain that the robot prioritizes enhancing human-robot trust in emergency scenarios to mitigate the long-term costs of human-robot collaboration.
Note to Practitioners
—The primary motivation of this paper lies in tackling the issue of decreased collaboration efficiency in human-robot cooperation, stemming from irrational human decision-making, a problem exacerbated in emergency scenarios where comprehending all available information proves challenging. Grounded in the realm of human-robot trust, this study evaluates the temporal and substantive aspects of human-robot interactions contingent upon the degree of trust. Moreover, it adapts the fusion decision-making process within the human-robot team in accordance with the decision-making strategies adopted by humans at varying trust levels. The optimized decisions of the human-robot ensemble are conveyed to the human participant via decision support. This approach ensures the maintenance of trust levels, facilitating the acceptance of decision support that may seem intuitively irrational but holds objective superiority. Furthermore, it mitigates redundant human-robot interactions once a sufficient level of trust is established.