Name
Abstract | ||
|---|---|---|
Contextual skill models are learned to provide skills over a range of task parameters, often using regression across optimal task-specific policies. However, the sequential nature of the learning process is usually neglected. In this paper, we propose to use active incremental learning by selecting a task which maximizes performance improvement over entire task set. The proposed framework exploits knowledge of individual tasks accumulated in a database and shares it among the tasks using a contextual skill model. The framework is agnostic to the type of policy representation, skill model, and policy search. We evaluated the skill improvement rate in two tasks, ball-in-a-cup and basketball. In both, active selection of tasks lead to a consistent improvement in skill performance over a baseline. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IROS40897.2019.8967837 | 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Field | DocType | ISSN |
Regression,Computer science,Incremental learning,Control engineering,Exploit,Artificial intelligence,Machine learning,Basketball,Performance improvement | Conference | 2153-0858 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Murtaza Hazara | 1 | 1 | 2.05 |
| Xiaopu Li | 2 | 0 | 1.01 |
| V. Kyrki | 3 | 652 | 61.79 |