Name
Abstract | ||
|---|---|---|
We describe a method of teaching a robot its empathic behavioural response from its interaction with people. We used the input modalities such as relative spatial information, facial expressions, body gestures and speech information as perception input that triggers the robot's empathic response. First, we bootstrap the training through a pre-learning mechanism in which training is conducted by users who know the robotic system. This phase provides simulation-based training using a simple graphical user interface to simulate the input, rewards and correction feedback. In the second phase, we developed an online learning scheme for naive users to personalize their robot further, building on top of the bootstrapped model. here, we developed a natural user interface that enables natural human-robot interaction via the suite of sensors that allows the users to provide evaluative feedback during the interaction with the robot. We evaluated the system and our results show that bootstrapping is an efficient tool to hasten the robot's learning while online learning provided some form of personalization in the real environment with naive users. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/RO-MAN50785.2021.9515525 | 2021 30TH IEEE INTERNATIONAL CONFERENCE ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (RO-MAN) |
DocType | ISSN | Citations |
Conference | 1944-9445 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yurii Vasylkiv | 1 | 0 | 3.38 |
| Zhen Ma | 2 | 0 | 0.68 |
| Guangliang Li | 3 | 0 | 0.68 |
| Heike Brock | 4 | 1 | 4.47 |
| Keisuke Nakamura | 5 | 189 | 28.91 |
| Pourang Irani | 6 | 0 | 0.34 |
| Randy Gomez | 7 | 76 | 28.11 |