Reinforcement Learning with Dynamic Movement Primitives for Obstacle Avoidance
文献类型：期刊论文
| 作者 | A. Li; Z. Z. Liu; W. R. Wang; M. C. Zhu; Y. H. Li; Q. Huo and M. Dai |
| 刊名 | Applied Sciences-Basel
 |
| 出版日期 | 2021 |
| 卷号 | 11期号:23页码:13 |
| DOI | 10.3390/app112311184 |
| 英文摘要 | Dynamic movement primitives (DMPs) are a robust framework for movement generation from demonstrations. This framework can be extended by adding a perturbing term to achieve obstacle avoidance without sacrificing stability. The additional term is usually constructed based on potential functions. Although different potentials are adopted to improve the performance of obstacle avoidance, the profiles of potentials are rarely incorporated into reinforcement learning (RL) framework. In this contribution, we present a RL based method to learn not only the profiles of potentials but also the shape parameters of a motion. The algorithm employed is PI2 (Policy Improvement with Path Integrals), a model-free, sampling-based learning method. By using the PI2, the profiles of potentials and the parameters of the DMPs are learned simultaneously; therefore, we can optimize obstacle avoidance while completing specified tasks. We validate the presented method in simulations and with a redundant robot arm in experiments. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/67031]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | A. Li,Z. Z. Liu,W. R. Wang,et al. Reinforcement Learning with Dynamic Movement Primitives for Obstacle Avoidance[J]. Applied Sciences-Basel,2021,11(23):13. |
| APA | A. Li,Z. Z. Liu,W. R. Wang,M. C. Zhu,Y. H. Li,&Q. Huo and M. Dai.(2021).Reinforcement Learning with Dynamic Movement Primitives for Obstacle Avoidance.Applied Sciences-Basel,11(23),13. |
| MLA | A. Li,et al."Reinforcement Learning with Dynamic Movement Primitives for Obstacle Avoidance".Applied Sciences-Basel 11.23(2021):13. |
入库方式： OAI收割
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