Enhancing Learning Capabilities of Movement Primitives under Distributed Probabilistic Framework for Assembly Tasks

Wang, Likun; Jia, Shuya; Wang, Guoyan; Turner, Alison; Ratchev, Svetan

Abstract

This paper presents a novel distributed probabilistic framework based on movement primitives for flexible robots assembly implementation. Since modern advanced industrial cell usually deals with various tasks that are not fixed via-point trajectories but highly reconfigurable application templates, the industrial robots used in these applications must be capable of adapting and learning new skills on-demand, without programming experts. Therefore, we propose a probabilistic framework that could accommodate various learning abilities trained with different movement-primitive datasets, separately. Thanks to the fusion theory of the Bayesian Committee Machine, this framework could infer new adapting trajectories with weighted contributions of every trained datasets. To verify the feasibility of our proposed imitation learning framework, state-of-the-art movement learning framework Task-parameterized GMM is compared from several crucial aspects, such as generalization capability, accuracy and robustness. Moreover. this framework is further tested on the YUMI collaborative robot with a rivet picking assembly scenario. Potential applications can be extended to more complicated industrial assembly manufacturing or service robotic applications.

Citation

Wang, L., Jia, S., Wang, G., Turner, A., & Ratchev, S. (2020). Enhancing Learning Capabilities of Movement Primitives under Distributed Probabilistic Framework for Assembly Tasks. . https://doi.org/10.1109/smc42975.2020.9283066

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
• Start Date: Oct 11, 2020
• End Date: Oct 14, 2020
• Acceptance Date: Aug 5, 2020
• Online Publication Date: Dec 14, 2020
• Publication Date: Oct 11, 2020
• Deposit Date: Mar 24, 2021
• Publicly Available Date: Mar 24, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 3832-3838
• Series Title: IEEE International Conference on Systems, Man, and Cybernetics
• Series ISSN: 1062-922X
• ISBN: 9781728185279
• DOI: https://doi.org/10.1109/smc42975.2020.9283066
• Public URL: https://nottingham-repository.worktribe.com/output/5413902
• Publisher URL: https://ieeexplore.ieee.org/document/9283066
• Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.