Name
Abstract | ||
|---|---|---|
We present a hierarchical planning and execution architecture that maintains the computational efficiency of hierarchical decomposition while improving optimality. It provides mechanisms for monitoring the belief state during execution and performing selective replanning to repair poor choices and take advantage of new opportunities. It also provides mechanisms for looking ahead into future plans to avoid making short-sighted choices. The effectiveness of this architecture is shown through comparative experiments in simulation and demonstrated on a real PR2 robot. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IROS.2013.6696357 | IROS |
Field | DocType | ISSN |
Architecture,Software engineering,Computer science,Control engineering,Futures studies,Artificial intelligence,Robot | Conference | 2153-0858 |
Citations | PageRank | References |
1 | 0.36 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Levihn | 1 | 24 | 4.12 |
| Leslie Pack Kaelbling | 2 | 5930 | 854.90 |
| Tomás Lozano-Pérez | 3 | 3882 | 1642.87 |
| Mike Stilman | 4 | 507 | 37.01 |