Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Enhanced task parameterized dynamic movement primitives by GMM to solve manipulation tasks

Enhanced task parameterized dynamic movement primitives by GMM to solve manipulation tasks Under the development trend of intelligent manufacturing, the unstructured environment requires the robot to have a good generalization performance to adapt to the scene changes. The purpose of this paper aims to present a learning from demonstration (LfD) method (task parameterized [TP]-dynamic movement primitives [DMP]-GMR) that combines DMPs and TP-LfD to improve generalization performance and solve object manipulation tasks.Design/methodology/approachThe dynamic time warping algorithm is applied to processing demonstration data to obtain a more standard learning model in the proposed method. The DMPs are used to model the basic trajectory learning model. The Gaussian mixture model is introduced to learn the force term of DMPs and solve the problem of learning from multiple demonstration trajectories. The robot can learn more local geometric features and generalize the learned model to unknown situations by adding task parameters.FindingsAn evaluation criterion based on curve similarity calculated by the Frechet distance was constructed to evaluate the model’s interpolation and extrapolation performance. The model’s generalization performance was assessed on 2D virtual data sets, and first, the results show that the proposed method has better interpolation and extrapolation performance than other methods.Originality/valueThe proposed model was applied to the axle-hole assembly task on real robots, and the robot’s posture in grasping and placing the axle part was taken as the task parameter of the model. The experiment results show that The proposed model is competitive with other models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Robotic Intelligence and Automation Emerald Publishing

Enhanced task parameterized dynamic movement primitives by GMM to solve manipulation tasks

Download PDF
11 pages

Loading next page...
 
/lp/emerald-publishing/enhanced-task-parameterized-dynamic-movement-primitives-by-gmm-to-iOq8ZIpQsr

References (29)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2754-6969
eISSN
2754-6977
DOI
10.1108/ria-07-2022-0199
Publisher site
See Article on Publisher Site

Abstract

Under the development trend of intelligent manufacturing, the unstructured environment requires the robot to have a good generalization performance to adapt to the scene changes. The purpose of this paper aims to present a learning from demonstration (LfD) method (task parameterized [TP]-dynamic movement primitives [DMP]-GMR) that combines DMPs and TP-LfD to improve generalization performance and solve object manipulation tasks.Design/methodology/approachThe dynamic time warping algorithm is applied to processing demonstration data to obtain a more standard learning model in the proposed method. The DMPs are used to model the basic trajectory learning model. The Gaussian mixture model is introduced to learn the force term of DMPs and solve the problem of learning from multiple demonstration trajectories. The robot can learn more local geometric features and generalize the learned model to unknown situations by adding task parameters.FindingsAn evaluation criterion based on curve similarity calculated by the Frechet distance was constructed to evaluate the model’s interpolation and extrapolation performance. The model’s generalization performance was assessed on 2D virtual data sets, and first, the results show that the proposed method has better interpolation and extrapolation performance than other methods.Originality/valueThe proposed model was applied to the axle-hole assembly task on real robots, and the robot’s posture in grasping and placing the axle part was taken as the task parameter of the model. The experiment results show that The proposed model is competitive with other models.

Journal

Robotic Intelligence and Automation – Emerald Publishing

Published: May 23, 2023

Keywords: Task parameterized learning from demonstration; Dynamic movement primitives; Gaussian mixture model; Dynamic time warping; Frechet distance

There are no references for this article.