Name
Abstract | ||
|---|---|---|
For robots to work outside of laboratory settings, their plans should be applicable to a variety of environments, objects, task contexts, and hardware platforms. This requires general-purpose methods that are, at this moment, not sufficiently performant for real-world applications. We propose an approach to specialize such general plans through running them for specific tasks and thereby learning ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LRA.2019.2928771 | IEEE Robotics and Automation Letters |
Keywords | Field | DocType |
Task analysis,Feature extraction,Search problems,Learning systems,Robot sensing systems,Grasping,Adaptive systems,Autonomous agents | Data modeling,Task analysis,Software engineering,Feature extraction,Control engineering,Supervised learning,Fetch,Systems architecture,Engineering,Robot | Journal |
Volume | Issue | ISSN |
4 | 4 | 2377-3766 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastian Koralewski | 1 | 2 | 2.43 |
| Gayane Kazhoyan | 2 | 2 | 3.77 |
| Michael Beetz | 3 | 3784 | 284.03 |