https://scholars.lib.ntu.edu.tw/handle/123456789/607269
標題: | Dual-Filtering for On-Line Simultaneously Estimate Weights and Phase Parameter of Probabilistic Movement Primitives for Human-Robot Collaboration | 作者: | REN-CHYUAN LUO Mai L. |
關鍵字: | Intelligent robots;Robotics;Base matrix;Human movements;Human-robot collaboration;Learning from demonstration;Movement primitives;Phase parameters;Prior distribution;Probabilistics;Skills acquisition;Weight parameters;Parameter estimation | 公開日期: | 2021 | 起(迄)頁: | 784-790 | 來源出版物: | IEEE International Conference on Intelligent Robots and Systems | 摘要: | The Probabilistic Movement Primitives (ProMPs) is an essential issue and framework for robotics Learning from Demonstration (LfD). It has been successfully applied to the robotics field in tasks such as skill acquisition and Human-Robot Collaboration (HRC). In this paper, we focus on its adaptability in the HRC scenario, in which the adaptability of the ProMPs allows the robot to predict the future movement of its human partner and plan its movement accordingly, given the observed human movement. Most of the existing works about the application of the ProMPs in HRC either only focus on the estimation of the weights on-line and lack the estimation of the phase parameter or merely depend on the prior distribution of the phase parameter. As a result, these methods can lead to a misinterpretation of the basis matrix when the divergence between the prior distribution and the posterior distribution of the phase parameter becomes large, resulting in a divergence of the estimation of the weights. In this paper, we propose a Dual-Filtering method for the ProMPs, which is able to simultaneously on-line estimate the weights and phase parameter for the ProMPs. The preliminary experimental result demonstrates the proposed method is able to provide better prediction performance and more accurate estimation of the phase parameter in comparison with the previous works. ? 2021 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124337704&doi=10.1109%2fIROS51168.2021.9636654&partnerID=40&md5=0c437bb6ec5bd69dd4baab962d74a701 https://scholars.lib.ntu.edu.tw/handle/123456789/607269 |
ISSN: | 21530858 | DOI: | 10.1109/IROS51168.2021.9636654 |
顯示於: | 電機工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。