高斯混合模型GMM 及高斯混合回归MATLAB程序

Task-parameterized tensor GMM with LQR Usage: Unzip the file and run 'demo01' in Matlab. Several reproduction algorithms can be selected by commenting/uncommenting lines 89-91 and 110-112 in demo01.m (finite/infinite horizon LQR or dynamical system with constant gains). 'demo_testLQR01' and 'demo_testLQR02' can also be run as examples of LQR. Reference: Calinon, S., Bruno, D. and Caldwell, D.G. (2014). A task-parameterized probabilistic model with minimal intervention control. Proc. of the IEEE Intl Conf. on Robotics and Automation (ICRA). Description: Demonstration a task-parameterized probabilistic model encoding movements in the form of virtual spring-damper systems acting in multiple frames of reference. Each candidate coordinate system observes a set of demonstrations from its own perspective, by extracting an attractor path whose variations depend on the relevance of the frame through the task. This information is exploited to generate a new attractor path corresponding to new situations (new positions and orientation of the frames), while the predicted covariances are exploited by a linear quadratic regulator (LQR) to estimate the stiffness and damping feedback terms of the spring-damper systems, resulting in a minimal intervention control strategy. Authors: Sylvain Calinon and Danilo Bruno, 2014 http://programming-by-demonstration.org/ This source code is given for free! In exchange, we would be grateful if you cite the following reference in any academic publication that uses this code or part of it: @inproceedings{Calinon14ICRA, author="Calinon, S. and Bruno, D. and Caldwell, D. G.", title="A task-parameterized probabilistic model with minimal intervention control", booktitle="Proc. {IEEE} Intl Conf. on Robotics and Automation ({ICRA})", year="2014", month="May-June", address="Hong Kong, China", pages="3339--3344" }