Name
Abstract | ||
|---|---|---|
When deploying a robot to a new task, one often has to train it to detect novel objects, which is time-consuming and labor-intensive. We present TAILOR - a method and system for object registration with active and incremental learning. When instructed by a human teacher to register an object, TAILOR is able to automatically select viewpoints to capture informative images by actively exploring viewpoints, and employs a fast incremental learning algorithm to learn new objects without potential forgetting of previously learned objects. We demonstrate the effectiveness of our method with a KUKA robot to learn novel objects used in a real-world gearbox assembly task through natural interactions. |
Year | Venue | DocType |
|---|---|---|
2021 | THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Conference |
Volume | ISSN | Citations |
35 | 2159-5399 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qianli Xu | 1 | 90 | 15.17 |
| Nicolas Gauthier | 2 | 0 | 2.03 |
| Wenyu Liang | 3 | 20 | 11.00 |
| Fen Fang | 4 | 0 | 2.03 |
| Hui Li Tan | 5 | 76 | 7.42 |
| Ying Sun | 6 | 291 | 40.03 |
| Yan Wu | 7 | 60 | 11.16 |
| Liyuan Li | 8 | 912 | 61.31 |
| Joo-Hwee Lim | 9 | 0 | 2.70 |