Name
Abstract | ||
|---|---|---|
Some robots can interact with humans using natural language, and identify service requests through human-robot dialog. However, few robots are able to improve their language capabilities from this experience. In this paper, we develop a dialog agent for robots that is able to interpret user commands using a semantic parser, while asking clarification questions using a probabilistic dialog manager. This dialog agent is able to augment its knowledge base and improve its language capabilities by learning from dialog experiences, e.g., adding new entities and learning new ways of referring to existing entities. We have extensively evaluated our dialog system in simulation as well as with human participants through MTurk and real-robot platforms. We demonstrate that our dialog agent performs better in efficiency and accuracy in comparison to baseline learning agents. Demo video can be found at https://youtu.be/DFB3jbHBqYE. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IROS40897.2019.8968269 | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Keywords | Field | DocType |
language capabilities,real-robot platforms,dialog agent,baseline learning agents,statistical goal-oriented human-robot dialog,natural language,probabilistic dialog manager,knowledge base | Dialog box,Computer science,Control engineering,Natural language,Human–computer interaction,Dialog system,Probabilistic logic,Knowledge base,Parsing,Robot,Human–robot interaction | Conference |
ISSN | ISBN | Citations |
2153-0858 | 978-1-7281-4005-6 | 1 |
PageRank | References | Authors |
0.37 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saeid Amiri | 1 | 1 | 0.70 |
| Sujay Bajracharya | 2 | 1 | 0.37 |
| Cihangir Goktolga | 3 | 1 | 0.37 |
| Jesse Thomason | 4 | 139 | 14.60 |
| Shiqi Zhang | 5 | 119 | 22.46 |