Name
Abstract | ||
|---|---|---|
Libraries of trajectories are a promising way of creating policies for difficult problems. However, often it is not desirable or even possible to create a new library for every task. We present a method for transferring libraries across tasks, which allows us to build libraries by learning from demonstration on one task and apply them to similar tasks. Representing the libraries in a feature-based space is key to supporting transfer. We also search through the library to ensure a complete path to the goal is possible. Results are shown for the Little Dog task. Little Dog is a quadruped robot that has to walk across rough terrain at reasonably fast speeds. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IROS.2007.4399364 | 2007 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-9 |
Keywords | Field | DocType |
robots,policies | Computer vision,Computer science,Terrain,Learning from demonstration,Artificial intelligence,Robot,Trajectory | Conference |
Citations | PageRank | References |
16 | 0.90 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Stolle | 1 | 47 | 2.23 |
| Hanns Tappeiner | 2 | 23 | 2.20 |
| Joel Chestnutt | 3 | 222 | 14.96 |
| Christopher G. Atkeson | 4 | 5441 | 849.86 |