Autonomous Speech Volume Control for Social Robots in a Noisy Environment Using Deep Reinforcement Learning This paper presents a novel approach to automatically adjusting the speech volume of a socially assistive humanoid robot to enhance the quality of humanrobot interactions. We apply the Deep Qlearning algorithm to enable the robot to adapt to the preferences of a user in the volume of the robotxe2x80x99s voice in social contexts. Subjective experiments were conducted to verify the validity of the proposed system. Twentythree human subjects had social conversations with humanoid robots across various noisy environments. Participants rated their perception of the robotsxe2x80x99 voices in terms of clearness and comfortability through a questionnaire. The results show that the robot equipped with our framework outperforms other experimental robots in trials. This study confirmed the effectiveness of the proposed autonomous speech volume control system for social robots communicating with people in noisy environments.

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.