Name
Title | ||
|---|---|---|
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction |
Abstract | ||
|---|---|---|
In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction. We present a meta-learning based policy gradient method for addressing the problem of adaptation in human-robot interaction and also investigate its role as a mechanism for trust modelling. By building an escape room scenario in mixed reality with a robot, we test our hypothesis that bi-directional trust can be influenced by different adaptation algorithms. We found that our proposed model increased the perceived trustworthiness of the robot and influenced the dynamics of gaining human's trust. Additionally, participants evaluated that the robot perceived them as more trustworthy during the interactions with the meta-learning based adaptation compared to the previously studied statistical adaptation model. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IROS40897.2019.8967924 | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Keywords | Field | DocType |
human trust,perceived trustworthiness,escape room scenario,statistical adaptation model,meta-learning based adaptation,bi-directional trust,meta-learning based policy gradient method,adaptation techniques,socially assistive robotics,human-robot interaction,trust modelling,meta-reinforcement learning | Gradient method,Computer science,Trustworthiness,Control engineering,Human–computer interaction,Artificial intelligence,Mixed reality,Robot,Human–robot interaction,Robotics,Reinforcement learning | Conference |
ISSN | ISBN | Citations |
2153-0858 | 978-1-7281-4005-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Gao | 1 | 2 | 1.43 |
| Elena Sibirtseva | 2 | 1 | 1.38 |
| Ginevra Castellano | 3 | 746 | 53.88 |
| Danica Kragic | 4 | 2070 | 142.17 |