AEXP4: Online Social Policy Learning for Adaptive RobotPedestrian Interaction We study selfsupervised adaptation of a robotu0027s policy for social interaction, i.e., a policy for active communication with surrounding pedestrians through audio or visual signals. Inspired by the observation that humans continually adapt their behavior when interacting under varying social context, we propose Adaptive EXP4 (AEXP4), a novel online learning algorithm for adapting the robotpedestrian interaction policy. To address limitations of bandit algorithms in adaptation to unseen and highly dynamic scenarios, we employ a mixture model over the policy parameter space. Specifically, a Dirichlet Process Gaussian Mixture Model (DPMM) is used to cluster the parameters of sampled policies and maintain a mixture model over the clusters, hence effectively discovering policies that are suitable to the current environmental context in an unsupervised manner. Our simulated and realworld experiments demonstrate the feasibility of AEXP4 in accommodating interaction with different types of pedestrians while jointly minimizing social disruption through the adaptation process. While the AEXP4 formulation is kept general for application in a variety of domains requiring continual adaptation of a robotu0027s policy, we specifically evaluate the performance of our algorithm using a suitcaseinspired assistive robotic platform. In this concrete assistive scenario, the algorithm observes how audio signals produced by the navigational system affect the behavior of pedestrians and adapts accordingly. Consequently, we find AEXP4 to effectively adapt the interaction policy for gently clearing a navigation path in crowded settings, resulting in significant reduction in empirical regret compared to the EXP4 baseline.

Fast Adaptation with MetaReinforcement Learning for Trust Modelling in HumanRobot Interaction In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on humanrobot interaction. We present a metalearning based policy gradient method for addressing the problem of adaptation in humanrobot 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 bidirectional 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 humanxe2x80x99s trust. Additionally, participants evaluated that the robot perceived them as more trustworthy during the interactions with the metalearning based adaptation compared to the previously studied statistical adaptation model.