Learning to Engage with Interactive Systems
A Field Study on Deep Reinforcement Learning in a Public Museum
Bibliographic Data
| ID | 22190876 |
|---|---|
| Authors | Lingheng Meng (0000-0001-9419-5774, University of Waterloo), Daiwei Lin (University of Waterloo), Adam Francey (University of Waterloo), Rob Gorbet (0000-0003-2799-3922, University of Waterloo), Philip Beesley (0000-0002-4747-9472, University of Waterloo), Dana Kulić (0000-0002-4169-2141, Monash University) |
| Year | 2021 |
| Volume | 10 |
| Issue | 1 |
| Pages | 1-29 |
| Publication date | 2021-03-31 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ACM Transactions on Human-Robot Interaction (JOURNAL) |
| Journal identifiers | ISSN: 2573-9522 • E-ISSN: 2573-9522 |
| Publisher | Association for Computing Machinery (ACM) (PUBLISHER) |
| DOI | 10.1145/3408876 |
| OpenAlex | W3096659177 |
| Language | EN |
| Citations received | 1 |
| References cited | 55 |
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
Human–computer interaction · Interactive Learning · Multimedia · Physical space · Reinforcement learning · Robot · Computer Science · Human Pose and Action Recognition · Robot Manipulation and Learning · Social Robot Interaction and HRI · Artificial Intelligence
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |