Name
Abstract | ||
|---|---|---|
Abstract The implementation of sequence learning in robotic platforms o ers several challenges. Deciding when to stop one action and continue to the next requires a balance between stability of sensory information and, of course, the knowledge about what action is required next. The work presented here proposes a starting point for the successful execution and learning of dynamic sequences. Making use of the NAO humanoid platform we propose a mathematical model based on dynamic field theory and reinforcement learning methods for obtaining and performing a sequence of elementary motor behaviors. Results from the comparison of two reinforcement learning methods applied to sequence generation, for both simulation and implementation, are provided. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.2478/s13230-013-0109-5 | Paladyn |
Field | DocType | Volume |
Robot learning,Simulation,Computer science,Artificial intelligence,Error-driven learning,Sequence learning,Machine learning,Reinforcement learning | Journal | 3 |
Issue | ISSN | Citations |
4 | 2081-4836 | 2 |
PageRank | References | Authors |
0.39 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Durán | 1 | 18 | 3.22 |
| Gauss Lee | 2 | 2 | 0.39 |
| Robert Lowe | 3 | 111 | 12.22 |