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Learning to Engage with Interactive Systems: A Field Study on Deep Reinforcement Learning in a Public Museum

Authors:

Philip Beesley,

Dana KulićAuthors Info & Claims

ACM Transactions on Human-Robot Interaction (THRI), Volume 10, Issue 1

Article No.: 5, Pages 1 - 29

https://doi.org/10.1145/3408876

Published: 20 October 2020 Publication History

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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.

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Learning to Engage with Interactive Systems: A Field Study on Deep Reinforcement Learning in a Public Museum

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cover image ACM Transactions on Human-Robot Interaction

ACM Transactions on Human-Robot Interaction Volume 10, Issue 1

Research Notes

March 2021

202 pages

EISSN:2573-9522

DOI:10.1145/3407734

Editors:
Odest Chadwicke Jenkins
University of Michigan, USA
,
Selma Sabanovic
Indiana University, USA

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Publication History

Published: 20 October 2020

Accepted: 01 June 2020

Revised: 01 March 2020

Received: 01 April 2019

Published in THRI Volume 10, Issue 1

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Cited By

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https://doi.org/10.1177/02783649241281369
Kim SAnthis JSebo SGrollman DBroadbent EJu WSoh HWilliams T(2024)A Taxonomy of Robot Autonomy for Human-Robot InteractionProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3634993(381-393)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610977.3634993
Ribeiro de Oliveira TBiancardi Rodrigues BMoura da Silva MAntonio N. Spinassé RGiesen Ludke GRuy Soares Gaudio MIglesias Rocha Gomes GGuio Cotini Lda Silva Vargens DQueiroz Schimidt MVarejão Andreão RMestria M(2023)Virtual Reality Solutions Employing Artificial Intelligence Methods: A Systematic Literature ReviewACM Computing Surveys10.1145/356502055:10(1-29)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3565020
Del Duchetto FHanheide M(2022)Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot InteractionsIEEE Robotics and Automation Letters10.1109/LRA.2022.31788077:3(6934-6941)Online publication date: Jul-2022
https://doi.org/10.1109/LRA.2022.3178807
Tong ZKulic DHammond TVerbert KParra DKnijnenburg BO'Donovan JTeale P(2021)Learning to Engage in Interactive Digital ArtProceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397481.3450691(275-279)Online publication date: 14-Apr-2021
https://dl.acm.org/doi/10.1145/3397481.3450691

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