The codes should be compatible with both Matlab and GNU Octave.

### Usage ###

### 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', 'demo_testLQR02' and 'demo_testLQR03' can also be run as additional examples of LQR.

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', 'demo_testLQR02' and 'demo_testLQR03' can also be run as additional examples of LQR.

### Reference ###

### 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).

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 ###

### 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.

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 ###

### 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:

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.",