  In socially assistive robotics, an important research area is the development
of adaptation techniques and their effect on human-robot interaction. We
present a meta-learning based policy gradient method for addressing the problem
of adaptation in human-robot interaction and also investigate its role as a
mechanism for trust modelling. By building an escape room scenario in mixed
reality with a robot, we test our hypothesis that bi-directional trust can be
influenced by different adaptation algorithms. We found that our proposed model
increased the perceived trustworthiness of the robot and influenced the
dynamics of gaining human's trust. Additionally, participants evaluated that
the robot perceived them as more trustworthy during the interactions with the
meta-learning based adaptation compared to the previously studied statistical
adaptation model.
