Name
Abstract | ||
|---|---|---|
This letter considers the problem of how robots in long-term space operations can learn to choose appropriate sources of assistance to recover from failures. Current assistant selection methods for failure handling are based on manually specified static lookup tables or policies, which are not responsive to dynamic environments or uncertainty in human performance. We describe a novel and highly fl... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LRA.2018.2801468 | IEEE Robotics and Automation Letters |
Keywords | Field | DocType |
Robots,Task analysis,Resource management,Monitoring,Space missions,Heuristic algorithms,Earth | Engineering management,Policy learning,Control engineering,Engineering | Journal |
Volume | Issue | Citations |
3 | 3 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steve McGuire | 1 | 4 | 1.78 |
| P. Michael Furlong | 2 | 15 | 2.89 |
| Christoffer R. Heckman | 3 | 12 | 10.78 |
| Simon Justin Julier | 4 | 38 | 10.49 |
| Daniel Szafir | 5 | 230 | 23.05 |
| Nisar R. Ahmed | 6 | 2 | 2.41 |