Motor Skills Learning and Generalization with Adapted Curvilinear Gaussian Mixture Model

This paper is intended to solve the motor skills learning, representation and generalization problems in robot imitation learning. To this end, we present an Adapted Curvilinear Gaussian Mixture Model (AdC-GMM) , which is a general extension of the GMM. The proposed model can encode data more compac...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of intelligent & robotic systems Ročník 96; číslo 3-4; s. 457 - 475
Hlavní autori: Zhang, Huiwen, Leng, Yuquan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.12.2019
Springer
Springer Nature B.V
Predmet:
Algorithms
Artificial Intelligence
Comparative analysis
Computer simulation
Control
Economic models
Electrical Engineering
Engineering
Entropy (Information theory)
Gaussian process
Gaussian processes
Hammers
Learning
Linearity
Mechanical Engineering
Mechatronics
Motor ability
Optimization
Parameter estimation
Parameterization
Parameters
Probabilistic models
Regression models
Representations
Robot learning
Robotics
Robots
Simulation
Skills
Trajectories
Cross entropy
Imitation learning
Curvilinear Gaussian model
Skill learning
ISSN:0921-0296, 1573-0409
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This paper is intended to solve the motor skills learning, representation and generalization problems in robot imitation learning. To this end, we present an Adapted Curvilinear Gaussian Mixture Model (AdC-GMM) , which is a general extension of the GMM. The proposed model can encode data more compactly. More critically, it is inherently suitable for representing data with strong non-linearity. To infer the parameters of this model, a Cross Entropy Optimization (CEO) algorithm is proposed, where the cross entropy loss of the training data is minimized. Compared with the traditional Expectation Maximization (EM) algorithm, the CEO can automatically infer the optimal number of components. Finally, the generalized trajectories are retrieved by an Adapted Curvilinear Gaussian Mixture Regression (AdC-GMR) model. To encode observations from different frames, the sophisticated task parameterization (TP) technique is introduced. All above proposed algorithms are verified by comprehensive tasks. The CEO is evaluated by a hand writing task. Another goal-directed reaching task is used to evaluate the AdC-GMM and AdC-GMR algorithm. A novel hammer-over-a-nail task is designed to verify the task parameterization technique. Experimental results demonstrate the proposed CEO is superior to the EM in terms of encoding accuracy and the AdC-GMM can achieve more compact representation by reducing the number of components by up to 50%. In addition, the trajectory retrieved by the AdC-GMR is smoother and the approximation error is comparable to the Gaussian process regression (GPR) even far fewer parameters need to be estimated. Because of this, the AdC-GMR is much faster than the GPR. Finally, simulation experiments on the hammer-over-a-nail task demonstrates the proposed methods can be deployed and used in real-world applications.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-019-00999-y