Paper Info
Title
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.
Year
DOI
Venue
2017
10.1145/2909824.3020217
HRI
Keywords
Field
DocType
Reinforcement Learning,Assistive Robotics,Multimodal Behavior Generation,Human-Robot Interaction
Robot learning,Social robot,Social behavior,Task analysis,Simulation,Computer science,Human–computer interaction,Artificial intelligence,Robot,Human–robot interaction,Reinforcement learning,Executable
Conference
ISSN
ISBN
Citations 
2167-2121
978-1-4503-4336-7
7
PageRank 
References 
Authors
0.49
28
2
Name
Order
Citations
PageRank
Jacqueline Hemminghaus170.49
stefan kopp29314.14