Complex Sequential Tasks Learning with Bayesian Inference and Gaussian Mixture Model
文献类型：会议论文
| 作者 | Zhang HW(张会文)1,2,3 ; Han XN(韩小宁)1,2,3; Zhang W(张伟)1
|
| 出版日期 | 2018 |
| 会议日期 | December 12-15, 2018 |
| 会议地点 | Kuala Lumpur, Malaysia |
| 关键词 | Bayesian segmentation dynamical system GMM movement primitives |
| 页码 | 1927-1934 |
| 英文摘要 | Transferring skills to robots by demonstrations has been extensively researched for decades. However, the majority of the work focuses on individual or low-level task learning. Theories and applications for learning complex sequential tasks are not well-investigated. For this reason, this paper presents a unified top-down framework for complex tasks learning. Specifically, we conclude two critical objectives. First, a segmentation algorithm which can segment unstructured demonstrations into movement primitives (MPs) with minimal prior knowledge requirements needs to be proposed. Second, choosing a representation model used to jointly extract tasks constraints from the discovered MPs. To achieve the first goal, a change-point detection algorithm based on Bayesian inference is used. It can segment unstructured demonstrations online. Then, we propose to model MPs with dynamical system approximated by the Gaussian mixture models (GMMs), which is flexible and powerful in movement representation. Finally, the whole framework is evaluated by an open-and-place task on a real robot. Experiments show the segmentation accuracy can reach to 95.6% and the task can be replayed in new contexts successfully. |
| 产权排序 | 1 |
| 会议录 | Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-0376-1 |
| WOS记录号 | WOS:000468772200303 |
| 源URL | [http://ir.sia.cn/handle/173321/24656]  |
| 专题 | 沈阳自动化研究所_空间自动化技术研究室 |
| 通讯作者 | Zhang HW(张会文) |
| 作者单位 | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.University of Chinese Academy of Sciences, Beijing 100049, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China |
| 推荐引用方式 GB/T 7714 | Zhang HW,Han XN,Zhang W. Complex Sequential Tasks Learning with Bayesian Inference and Gaussian Mixture Model[C]. 见:. Kuala Lumpur, Malaysia. December 12-15, 2018. |
入库方式： OAI收割
来源：沈阳自动化研究所
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