A ReinforcementLearning Approach for Adaptive and Comfortable Assistive Robot Monitoring Behavior Companion robots used in the field of elderly assistive care can be of great value in monitoring their everyday activities and wellbeing. However, in order to be accepted by the user, their behavior, while monitoring them, should not provide discomfort: robots must take into account the activity the user is performing and not be a distraction for them. In this paper, we propose a Reinforcement Learning approach to adaptively decide a monitoring distance and an approaching direction starting from an estimation of the current activity obtained by the use of a wearable device. Our goal is to improve user activity recognition performance without making the robotxe2x80x99s presence uncomfortable for the monitored person. Results show that the proposed approach is promising for real scenario deployment, succeeding in accomplishing the task in more than 80%of episodes run.

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.