Name
Abstract | ||
|---|---|---|
For legged robot walking pattern learning, the current mainstream and state-of-the-art researches are most under a so called computer simulation based framework, where the walking pattern is learned via a pre-established simulation platform. However, when the learned walking pattern is applied to a real robot, an additional adapting procedure is always required, due to the big difference between simulation and real walking circumstances. This turns out to be more critical for a bipedal walking, because its controlling is more difficult than others, such as quadruped robot. In this paper, a novel framework for active online learning bipedal walking directly on a physical robot is proposed. To let the learning procedure to be of both fast convergence and high efficiency, a polynomial response surrogate model, an orthogonal experimental design based active learning strategy as well as a gradient ascent algorithm are used. The experimental results on a real humanoid robot PKU-HR3 show its effectiveness, indicating that the proposed learning framework is a promising alternative for bipedal walking pattern learning. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/Humanoids.2011.6100850 | 2011 11th IEEE-RAS International Conference on Humanoid Robots |
Keywords | Field | DocType |
humanoid robot,bipedal walking pattern,online learning,surrogate model,active learning | Robot learning,Convergence (routing),Gradient descent,Active learning,Simulation,Computer science,Legged robot,Surrogate model,Artificial intelligence,Robot,Humanoid robot | Conference |
Volume | Issue | ISSN |
null | null | 2164-0572 |
ISBN | Citations | PageRank |
978-1-61284-866-2 | 2 | 0.37 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dingsheng Luo | 1 | 46 | 11.61 |
| Yi Wang | 2 | 3 | 2.07 |
| Xihong Wu | 3 | 279 | 53.02 |