scholarly journals Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ning Wang ◽  
Yang Xu ◽  
Hongbin Ma ◽  
Xiaofeng Liu
Keyword(s):  
Muscle Fatigue ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Robot Learning ◽  
Movement Trajectory ◽  
Mixture Regression ◽  
Learning Tasks ◽  
Training Stage ◽  
Short Time ◽  
Semg Signals

To investigate the effects of muscle fatigue on bioinspired robot learning quality in teaching by demonstration (TbD) tasks, in this work, we propose to first identify the emerging muscle fatigue phenomenon of the human demonstrator by analyzing his/her surface Electromyography (sEMG) recordings and then guide the robot learning curve with this knowledge in mind. The time-varying amplitude and frequency sequences determining the subband sEMG signals have been estimated and their dominant values over short time intervals have been explored as fatigue-indicating features. These features are found carrying muscle fatigue cues of the human demonstrator in the course of robot manipulation. In robot learning tasks requiring multiple demonstrations, the fatiguing status of human demonstrator can be acquired by tracking the changes of the proposed features over time. In order to model data from multiple demonstrations, Gaussian mixture models (GMMs) have been employed. According to the identified muscle fatigue factor, a weight has been assigned to each of the demonstration trials in training stage, which is therefore termed as weighted GMMs (W-GMMs) algorithm. Six groups of data with various fatiguing status, as well as their corresponding weights, are taken as input data to get the adapted W-GMMs parameters. After that, Gaussian mixture regression (GMR) algorithm has been applied to regenerate the movement trajectory for the robot. TbD experiments on Baxter robot with 30 human demonstration trials show that the robot can successfully accomplish the taught task with a generated trajectory much closer to that of the desirable condition where little fatigue exists.

Modeling with Gaussian mixture regression for lactationmilk yield in Anatolian buffaloes

2016 ◽  
Author(s):  
Abdullah Yesilova ◽  
Ayhan Yilmaz ◽  
Gazel Ser ◽  
Baris Kaki
Keyword(s):  
Milk Yield ◽  
Environmental Effects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Lactation Period ◽  
Multivariate Statistical ◽  
Yield Data ◽  
Data Set ◽  
Mixture Regression ◽  
Lactation Duration

The purpose of this study was to classify Anatolian buffalo using Gaussian mixture regression model according to discrete and continuous environmental effects. Gaussian mixture model performs separately regression analysis both within and between groups. This is an important property of Gaussian mixture models which makes it different from other multivariate statistical methods. The data were obtained from 1455 Anatolian buffalo lactation milk yield records reared in seven different locations in Bitlis province, Turkey. Age of dam, lactation duration and locations were considered as environmental effects on lactation milk yield. Data set was divided into three homogenous subgroups with respect to AIC and BIC in the Gaussian mixture regression, based on environmental effects on lactation milk yield. Estimated mean for lactation milk yields and mixing probabilities for the first, second and third subgroups were determined as 1494.33 kg (16.9%), 540.33 kg (45.2%) and 847.61 (37.9%), respectively. The numbers of buffalo in each subgroup according to mixing probability were obtained as 159, 756, and 540 for the first, second, and third groups, respectively. The effects of lactation period, age of dam and villages were found statistically significant on lactation milk yield in subgroup 1 that was highest mean for lactation milk yield (p less than 0.01). In conclusion, results showed that Gaussian mixture regression was an important tool for classifying quantitative traits considering environmental effects in animal breeding.

scholarly journals ROBOT LEARNING OF OBJECT MANIPULATION TASK ACTIONS FROM HUMAN DEMONSTRATIONS

2017 ◽  
Vol 15 (2) ◽  
pp. 217 ◽  
Author(s):  
Maria Kyrarini ◽  
Muhammad Abdul Haseeb ◽  
Danijela Ristić-Durrant ◽  
Axel Gräser
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Object Manipulation ◽  
Gaussian Mixture ◽  
Robot Learning ◽  
Time Warping ◽  
Mixture Regression ◽  
Manipulation Task ◽  
Dynamic Time ◽  
High Level

Robot learning from demonstration is a method which enables robots to learn in a similar way as humans. In this paper, a framework that enables robots to learn from multiple human demonstrations via kinesthetic teaching is presented. The subject of learning is a high-level sequence of actions, as well as the low-level trajectories necessary to be followed by the robot to perform the object manipulation task. The multiple human demonstrations are recorded and only the most similar demonstrations are selected for robot learning. The high-level learning module identifies the sequence of actions of the demonstrated task. Using Dynamic Time Warping (DTW) and Gaussian Mixture Model (GMM), the model of demonstrated trajectories is learned. The learned trajectory is generated by Gaussian mixture regression (GMR) from the learned Gaussian mixture model.  In online working phase, the sequence of actions is identified and experimental results show that the robot performs the learned task successfully.

scholarly journals An Extended DMPs framework for Decoupled Quaternions Learning and generalization

2021 ◽  
Author(s):  
Zhiwei Liao ◽  
Fei Zhao ◽  
Gedong Jiang ◽  
Xuesong Mei
Keyword(s):  
Riemannian Manifolds ◽  
Gaussian Mixture ◽  
Joint Space ◽  
Robot Learning ◽  
Learning From Demonstration ◽  
End Effector ◽  
Mixture Regression ◽  
Cartesian Space ◽  
Dynamic Movement ◽  
Dynamic Movement Primitives

Abstract Dynamic Movement Primitives (DMPs) as a robust and efficient framework has been studied widely for robot learning from demonstration. Classical DMPs framework mainly focuses on the movement learning in Cartesian or joint space, and can't properly represent end-effector orientation. In this paper, we present an Extended DMPs framework (EDMPs) both in Cartesian space and Riemannian manifolds for Quaternion-based orientations learning and generalization. Gaussian Mixture Model and Gaussian Mixture Regression are adopted as the initialization phase of EDMPs to handle multi-demonstrations and obtain their mean and covariance. Additionally, some evaluation indicators including reachability and similarity are defined to characterize the learning and generalization abilities of EDMPs. Finally, the quaternion-based orientations are successfully transferred from human to the robot, and a real-world experiment is conducted to verify the effectiveness of the proposed method. The experimental results reveal that the presented approach can learn and generalize multi-space parameters under multi-demonstrations.

scholarly journals Measure of Similarity between GMMs by Embedding of the Parameter Space That Preserves KL Divergence

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 957
Author(s):  
Branislav Popović ◽  
Lenka Cepova ◽  
Robert Cep ◽  
Marko Janev ◽  
Lidija Krstanović
Keyword(s):  
Computational Complexity ◽  
Parameter Space ◽  
Recognition Accuracy ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Dimensional Manifold ◽  
Dimensional Parameter ◽  
Trade Off ◽  
Measure Of Similarity ◽  
Lower Dimensional

In this work, we deliver a novel measure of similarity between Gaussian mixture models (GMMs) by neighborhood preserving embedding (NPE) of the parameter space, that projects components of GMMs, which by our assumption lie close to lower dimensional manifold. By doing so, we obtain a transformation from the original high-dimensional parameter space, into a much lower-dimensional resulting parameter space. Therefore, resolving the distance between two GMMs is reduced to (taking the account of the corresponding weights) calculating the distance between sets of lower-dimensional Euclidean vectors. Much better trade-off between the recognition accuracy and the computational complexity is achieved in comparison to measures utilizing distances between Gaussian components evaluated in the original parameter space. The proposed measure is much more efficient in machine learning tasks that operate on large data sets, as in such tasks, the required number of overall Gaussian components is always large. Artificial, as well as real-world experiments are conducted, showing much better trade-off between recognition accuracy and computational complexity of the proposed measure, in comparison to all baseline measures of similarity between GMMs tested in this paper.

scholarly journals A Unified Formulation of k-Means, Fuzzy c-Means and Gaussian Mixture Model by the Kolmogorov–Nagumo Average

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 518
Author(s):  
Osamu Komori ◽  
Shinto Eguchi
Keyword(s):  
Pareto Distribution ◽  
Statistical Data ◽  
Learning Algorithm ◽  
Survival Function ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Simulation Studies ◽  
Probabilistic Framework ◽  
Underlying Distribution ◽  
Fuzzy C Means

Clustering is a major unsupervised learning algorithm and is widely applied in data mining and statistical data analyses. Typical examples include k-means, fuzzy c-means, and Gaussian mixture models, which are categorized into hard, soft, and model-based clusterings, respectively. We propose a new clustering, called Pareto clustering, based on the Kolmogorov–Nagumo average, which is defined by a survival function of the Pareto distribution. The proposed algorithm incorporates all the aforementioned clusterings plus maximum-entropy clustering. We introduce a probabilistic framework for the proposed method, in which the underlying distribution to give consistency is discussed. We build the minorize-maximization algorithm to estimate the parameters in Pareto clustering. We compare the performance with existing methods in simulation studies and in benchmark dataset analyses to demonstrate its highly practical utilities.

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