Name
Abstract | ||
|---|---|---|
Optimizing policies for real-time control of humanoid robots is a difficult task due to the continuous and stochastic nature of the state and action spaces. In this paper, we propose a learning procedure to train a predictive motion model and RFPI, a solver for continuous-state and action MDP. We use the predictive model as a transition model to train policies for a robot soccer. Our method requires no external hardware, a small amount of human work and manages to outperform the expert policy used by our team Rhoban winning the last 2016 edition of the Robocup in kid-size soccer league. Moreover, the proposed method is able to adapt to non-holonomic robots more efficiently than the expert approach. Our results are confirmed by both simulations and real robot experiments. |
Year | Venue | Field |
|---|---|---|
2017 | Proceedings of the International Conference on Automated Planning and Scheduling | Computer science,Simulation,Human–computer interaction,Artificial intelligence,Machine learning,Humanoid robot |
DocType | ISSN | Citations |
Conference | 2334-0835 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ludovic Hofer | 1 | 0 | 1.69 |
| Quentin Rouxel | 2 | 3 | 1.75 |