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Bagging for Gaussian mixture regression in robot learning from demonstration
Journal of Intelligent Manufacturing ( IF 8.3 ) Pub Date : 2020-10-26 , DOI: 10.1007/s10845-020-01686-8
Congcong Ye , Jixiang Yang , Han Ding

Robot learning from demonstration (LfD) emerges as a promising solution to transfer human motion to the robot. However, because of the open-loop between the learner and task constraints, the precision of the reproduction at the desired task constraints cannot always be guaranteed and the model is not robust to changes of the training data. This paper proposes a closed-loop framework of LfD based on the bagging method of Gaussian Mixture Model and Gaussian Mixture Regression (GMM/GMR) to obtain a robust learner of LfD with high precision reproduction. The original demonstration data are divided into several sub-training data, from which multiple Gaussian mixture models are developed and combined through weighted average to provide predictions. A closed-loop is built between the reproduction of the combined learner and task constraints, and the weights that satisfy task constraints are estimated in the closed-loop. The prediction uncertainty of the models is automatically eliminated by the closed-loop, therefore, the low robustness of the LfD model to the training date is overcome. In experiments, tasks of the position and velocity are both constrained in dual closed-loop. It is shown that the proposed method can significantly meet the task constraints without increasing the complexity of the algorithm.



中文翻译:

从演示中学习机器人的袋装高斯混合回归

演示中的机器人学习(LfD)成为将人类运动传递给机器人的有前途的解决方案。但是,由于学习者和任务约束之间存在开环,因此无法始终保证所需任务约束下的再现精度,并且该模型对于训练数据的更改也不可靠。本文基于高斯混合模型和高斯混合回归(GMM / GMR)的装袋方法,提出了一种LfD的闭环框架,以获得一个鲁棒的LfD学习器。具有高精度的复制。原始的演示数据被分为几个子训练数据,从中可以开发出多个高斯混合模型,并通过加权平均值进行组合以提供预测。在组合学习者的再现和任务约束之间建立一个闭环,并在闭环中估计满足任务约束的权重。闭环自动消除了模型的预测不确定性,因此克服了LfD模型对训练日期的鲁棒性。在实验中,位置和速度的任务都被限制在双重闭环中。结果表明,该方法在不增加算法复杂度的情况下,可以明显满足任务约束。

更新日期:2020-10-27
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