Name
Abstract | ||
|---|---|---|
This study proposes an approximate parametric model-based Bayesian reinforcement learning approach for robots, based on online Bayesian estimation and online planning for an estimated model. The proposed approach is designed to learn a robotic task with a few real-world samples and to be robust against model uncertainty, within feasible computational resources. The proposed approach employs two-stage modeling, which is composed of (1) a parametric differential equation model with a few parameters based on prior knowledge such as equations of motion, and (2) a parametric model that interpolates a finite number of transition probability models for online estimation and planning. The proposed approach modifies the online Bayesian estimation to be robust against approximation errors of the parametric model to a real plant. The policy planned for the interpolating model is proven to have a form of theoretical robustness. Numerical simulation and hardware experiments of a planar peg-in-hole task demonstrate the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10514-020-09901-4 | Autonomous Robots |
Keywords | DocType | Volume |
Model-based Bayesian reinforcement learning, Model uncertainty, Plant estimation, Interpolating model, Robustness of proper policy | Journal | 44 |
Issue | ISSN | Citations |
5 | 0929-5593 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kei Senda | 1 | 19 | 8.53 |
| Toru Hishinuma | 2 | 0 | 0.34 |
| Yurika Tani | 3 | 0 | 0.34 |