Commit 46e83db2 authored by Sylvain Calinon's avatar Sylvain Calinon

Update README.md

parent b9f8f7a3
### Task-parameterized tensor GMM with LQR ###
# Task-parameterized tensor GMM with LQR
### Compatibility ###
### Compatibility
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.",
......
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