Adaptive linguistic style for an assistive robotic health companion based on explicit human feedback

Abstract

In the future, an increasing amount of social robots will be found in our domestic environments to support and facilitate everyday life. Especially in the context of assistive, health-related support, more and more robotic products are on the way to the consumer market. While one can observe that many commercial efforts are put into the visual appearance, embodiment, motion and sound of companion robots, this paper focuses on the robot's conversational skills. We investigate how to adapt the robot's linguistic style to the individual user's preferences. This includes two forms of robot persona in the context of information retrieval tasks and games, as well as politeness with regard to recommendations. Therefore, we present an autonomous companion robot, which adapts its spoken language based on explicit human feedback. It provides several functionalities for information retrieval, reminders, communication and entertainment as well as health-related recommendations. Results of the in-situ study with elderly participants indicate that human preferences vary with regard to the robot's employed politeness strategies. Furthermore, the participants preferred assistant persona over companion persona in the information retrieval context. CCS CONCEPTS • Computing methodologies → Intelligent agents; Reinforcement learning; • Human-centered computing → Haptic devices;

Insights on usability and user feedback for an assistive robotic health companion with adaptive linguistic style

“…Inspired by [8] and [7] we built an assistive companion [6] with adaptive linguistic style in order to investigate the role and impact of the robot's communicative behavior. It is equipped with games, jokes, health-related recommendations, information retrieval for news, weather forecast, appointments and contacts as well as email and chat communication.…”

Reinforcement Learning Approaches in Social Robotics

“…In order to facilitate natural interaction, researchers in social robotics have focused on robots that can adapt to diverse conditions and to different user needs. Recently, there has been great interest in the use of machine learning methods for adaptive social robots [ 1 , 2 , 3 , 4 ]. Machine Learning (ML) algorithms can be categorized into three sub fields: supervised learning, unsupervised learning and reinforcement learning.…”

Multimodal Joke Generation and Paralinguistic Personalization for a Socially-Aware Robot

“…Machine learning is used successfully to adapt the robot's show to the spectators' preferences by selecting scripted contents intelligently. Reinforcement Learning (RL) has become very popular for optimizing social robots' linguistic contents [31,28,30] in recent years, also with focus on humor [37,38].…”