Name
Abstract | ||
|---|---|---|
This work presents an approach for learning navigation behaviors for robots using Optimal Rapidly-exploring Random Trees (RRT) as the main planner. A new learning algorithm combining both Inverse Reinforcement Learning and RRT is developed to learn the RRT’s cost function from demonstrations. A comparison with other state-of-the-art algorithms shows how the method can recover the behavior from the demonstrations. Finally, a learned cost function for social navigation is tested in real experiments with a robot in the laboratory. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s12369-017-0448-1 | I. J. Social Robotics |
Keywords | Field | DocType |
Path planning,Learning from demonstration,Social robots | Motion planning,Social robot,Simulation,Planner,Psychology,Learning from demonstration,Inverse reinforcement learning,Artificial intelligence,Robot,Social navigation | Journal |
Volume | Issue | ISSN |
10 | 2 | 1875-4791 |
Citations | PageRank | References |
0 | 0.34 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noé Pérez-Higueras | 1 | 8 | 1.86 |
| Fernando Caballero | 2 | 610 | 45.38 |
| Luis Merino | 3 | 325 | 26.09 |