A Reinforcement-Learning Approach for Adaptive and Comfortable Assistive Robot Monitoring Behavior

Abstract

Companion robots used in the field of elderly assistive care can be of great value in monitoring their everyday activities and well-being. 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 robot's 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.