# Task-parameterized tensor GMM with LQR
### Compatibility
The codes should be compatible with both Matlab and GNU Octave.
### Usage
Unzip the file and run 'demo_TPGMR_LQR01' (finite horizon LQR), 'demo_TPGMR_LQR02' (infinite horizon LQR) or
'demo_DSGMR01' (dynamical system with constant gains) in Matlab.
'demo_testLQR01', 'demo_testLQR02' and 'demo_testLQR03' can also be run as additional 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"
}