Learning to Engage with Interactive Systems

Author:

Meng Lingheng1,Lin Daiwei1,Francey Adam1,Gorbet Rob1,Beesley Philip1,Kulić Dana2

Affiliation:

1. University of Waterloo, Waterloo, Ontario, Canada

2. University of Waterloo and Monash University, VIC, Australia

Abstract

Physical agents that can autonomously generate engaging, life-like behavior will lead to more responsive and user-friendly robots and other autonomous systems. Although many advances have been made for one-to-one interactions in well-controlled settings, physical agents should be capable of interacting with humans in natural settings, including group interaction. To generate engaging behaviors, the autonomous system must first be able to estimate its human partners’ engagement level. In this article, we propose an approach for estimating engagement during group interaction by simultaneously taking into account active and passive interaction, and use the measure as the reward signal within a reinforcement learning framework to learn engaging interactive behaviors. The proposed approach is implemented in an interactive sculptural system in a museum setting. We compare the learning system to a baseline using pre-scripted interactive behaviors. Analysis based on sensory data and survey data shows that adaptable behaviors within an expert-designed action space can achieve higher engagement and likeability.

Funder

Social Sciences and Humanities Research Council of Canada

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Human-Computer Interaction

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Taxonomy of Robot Autonomy for Human-Robot Interaction;Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction;2024-03-11

2. Virtual Reality Solutions Employing Artificial Intelligence Methods: A Systematic Literature Review;ACM Computing Surveys;2023-02-02

3. Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions;IEEE Robotics and Automation Letters;2022-07

4. Systematic Review of Virtual Reality Solutions Employing Artificial Intelligence Methods;Symposium on Virtual and Augmented Reality;2021-10-18

5. Memory-based Deep Reinforcement Learning for POMDPs;2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2021-09-27

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3