Towards Adaptive Social Behavior Generation for Assistive Robots Using Reinforcement Learning

Abstract

In this paper we explore whether a social robot can learn, in and from a task-oriented interaction with a human user, how to employ different social behaviors to achieve interactional goals under specific situational circumstances. We present a multimodal behavior generation architecture that maps high-level interactional functions and behaviors onto low-level behaviors executable by a robot. While high-level behaviors are selected based on the state of the user as well as the interaction, reinforcement learning is used within each behavior to optimize its local mapping onto lower-level behaviors. The approach is implemented and applied in a scenario in which a social robot (Furhat) assists a human player in solving a Memory game by guiding the attention of the user to target objects. Results of an evaluation study demonstrate that participants are able to solve the Memory faster with the adaptive, assistive robot.

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