Forming robot trust in heterogeneous agents during a multimodal interactive game

This study presents a robot trust model based on cognitive load that uses multimodal cues in a learning setting to assess the trustworthiness of heterogeneous interaction partners. As a test-bed, we designed an interactive task where a small humanoid robot, Nao, is asked to perform a sequential audio-visual pattern recall task while minimizing its cognitive load by receiving help from its interaction partner, either a robot, Pepper, or a human. The partner displayed one of three guiding strategies, reliable, unreliable, or random. The robot is equipped with two cognitive modules: a multimodal auto-associative memory and an internal reward module. The former represents the multimodal cognitive processing of the robot and allows a 'cognitive load' or 'cost' to be assigned to the processing that takes place, while the latter converts the cognitive processing cost to an internal reward signal that drives the cost-based behavior learning. Here, the robot asks for help from its interaction partner when its action leads to a high cognitive load. Then the robot receives an action suggestion from the partner and follows it. After performing interactive experiments with each partner, the robot uses the cognitive load yielded during the interaction to assess the trustworthiness of the partners -i.e., it associates high trustworthiness with low cognitive load. We then give a free choice to the robot to select the trustworthy interaction partner to perform the next task. Our results show that, overall, the robot selects partners with reliable guiding strategies. Moreover, the robot's ability to identify a trustworthy partner was unaffected by whether the partner was a human or a robot.