SIA OpenIR  > 空间自动化技术研究室
Complex Sequential Tasks Learning with Bayesian Inference and Gaussian Mixture Model
Zhang HW(张会文)1,2,3; Han XN(韩小宁)1,2,3; Zhang W(张伟)1; Zhou WJ(周维佳)1
Department空间自动化技术研究室
Conference Name2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Conference DateDecember 12-15, 2018
Conference PlaceKuala Lumpur, Malaysia
Source PublicationProceedings of the 2018 IEEE International Conference on Robotics and Biomimetics
PublisherIEEE
Publication PlaceNew York
2018
Pages1927-1934
Indexed ByEI ; CPCI(ISTP)
EI Accession number20191506772421
WOS IDWOS:000468772200303
Contribution Rank1
ISBN978-1-7281-0376-1
KeywordBayesian segmentation dynamical system GMM movement primitives
AbstractTransferring 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.
Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/24656
Collection空间自动化技术研究室
Corresponding AuthorZhang HW(张会文)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Zhang HW,Han XN,Zhang W,et al. Complex Sequential Tasks Learning with Bayesian Inference and Gaussian Mixture Model[C]. New York:IEEE,2018:1927-1934.
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