Name
Title | ||
|---|---|---|
Multi-Modal Robot Apprenticeship: Imitation Learning Using Linearly Decayed Dmp Plus In A Human-Robot Dialogue System |
Abstract | ||
|---|---|---|
Robot learning by demonstration gives robots the ability to learn tasks which they have not been programmed to do before. The paradigm allows robots to work in a greater range of real-world applications in our daily life. However, this paradigm has traditionally been applied to learn tasks from a single demonstration modality. This restricts the approach to be scaled to learn and execute a series of tasks in a real-life environment. In this paper, we propose a multi-modal learning approach using DMP+ with linear decay integrated in a dialogue system with speech and ontology for the robot to learn seamlessly through natural interaction modalities (like an apprentice) while learning or re-learning is done on the fly to allow partial updates to a learned task to reduce potential user fatigue and operational downtime in teaching. The performance of new DMP+ with linear decay system is statistically benchmarked against state-of-the-art DMP implementations. A gluing demonstration is also conducted to show how the system provides seamless learning of multiple tasks in a flexible manufacturing set-up. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IROS.2018.8593634 | 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Field | DocType | ISSN |
Ontology (information science),Kernel (linear algebra),Robot learning,Ontology,Task analysis,Computer science,Control engineering,Human–computer interaction,Robot,Downtime,Human–robot interaction | Conference | 2153-0858 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Wu | 1 | 60 | 11.16 |
| Ruohan Wang | 2 | 11 | 2.71 |
| Luis Fernando D'Haro | 3 | 181 | 25.97 |
| Rafael E. Banchs | 4 | 566 | 63.64 |
| Keng-Peng Tee | 5 | 1062 | 59.75 |