Updated list of examples

README.md

@@ -41,7 +41,7 @@ https://gitlab.idiap.ch/rli/pbdlib_gui
 ### Test 
 
 ```
-cd build/examples
+cd examples
 ./test_gmm
 ```
 
@@ -72,3 +72,160 @@ In order to link your program with PbDLib and Armadillo, add the following comma
 find_library(ARMADILLO_LIBRARIES armadillo)
 target_link_libraries(yourProgram pbd ${ARMADILLO_LIBRARIES})
 ```
+
+### References
+
+Did you find PbDlib useful for your research? Please acknowledge the authors in any academic publications that used parts of these codes.
+<br><br>
+
+[1] Tutorial (GMM, TP-GMM, MFA, MPPCA, GMR, LWR, GPR, MPC, LQR, trajGMM):
+```
+@article{Calinon16JIST,
+	author="Calinon, S.",
+	title="A Tutorial on Task-Parameterized Movement Learning and Retrieval",
+	journal="Intelligent Service Robotics",
+	publisher="Springer Berlin Heidelberg",
+	year="2016",
+	volume="9",
+	number="1",
+	pages="1--29",
+	doi="10.1007/s11370-015-0187-9",
+}
+```
+
+[2] HMM, HSMM:
+```
+@article{Rozo16Frontiers,
+	author="Rozo, L. and Silv\'erio, J. and Calinon, S. and Caldwell, D. G.",
+	title="Learning Controllers for Reactive and Proactive Behaviors in Human-Robot Collaboration",
+	journal="Frontiers in Robotics and {AI}",
+	year="2016",
+	month="June",
+	volume="3",
+	number="30",
+	pages="1--11",
+	doi="10.3389/frobt.2016.00030"
+}
+```
+
+[3] Riemannian manifolds (S2,S3):
+```
+@article{Zeestraten17RAL,
+	author="Zeestraten, M. J. A. and Havoutis, I. and Silv\'erio, J. and Calinon, S. and Caldwell, D. G.",
+	title="An Approach for Imitation Learning on {R}iemannian Manifolds",
+	journal="{IEEE} Robotics and Automation Letters ({RA-L})",
+	year="2017",
+	month="June",
+	volume="2",
+	number="3",
+	pages="1240--1247"
+	doi="10.1109/LRA.2017.2657001",
+}
+```
+
+[4] Riemannian manifolds (S+):
+```
+@inproceedings{Jaquier17IROS,
+	author="Jaquier, N. and Calinon, S.", 
+	title="Gaussian Mixture Regression on Symmetric Positive Definite Matrices Manifolds: Application to Wrist Motion Estimation with {sEMG}",
+	booktitle="Proc. {IEEE/RSJ} Intl Conf. on Intelligent Robots and Systems ({IROS})",
+	year="2017",
+	month="September",
+	address="Vancouver, Canada",
+	pages=""
+}
+```
+
+[5] Semi-tied GMM:
+```
+@article{Tanwani16RAL,
+	author="Tanwani, A. K. and Calinon, S.",
+	title="Learning Robot Manipulation Tasks with Task-Parameterized Semi-Tied Hidden Semi-{M}arkov Model",
+	journal="{IEEE} Robotics and Automation Letters ({RA-L})",
+	year="2016",
+	month="January",
+	volume="1",
+	number="1",
+	pages="235--242",
+	doi="10.1109/LRA.2016.2517825"
+}
+```
+
+[6] DP-means:
+```
+@article{Bruno17AURO,
+	author="Bruno, D. and Calinon, S. and Caldwell, D. G.",
+	title="Learning Autonomous Behaviours for the Body of a Flexible Surgical Robot",
+	journal="Autonomous Robots",
+	year="2017",
+	month="February",
+	volume="41",
+	number="2",
+	pages="333--347",
+	doi="10.1007/s10514-016-9544-6"
+}
+```
+
+[7] Shared control, adaptive teleoperation:
+```
+@inproceedings{Havoutis17ICRA,
+	author="Havoutis, I. and Calinon, S.",
+	title="Supervisory teleoperation with online learning and optimal control",
+	booktitle=ICRA,
+	year="2017",
+	month="May-June",
+	address="Singapore",
+	pages="1534--1540"
+}
+```
+
+[8] Calligraphy, graffiti, handwriting movement generation:
+```
+@inproceedings{Berio17GI,
+	author="Berio, D. and Calinon, S. and Fol Leymarie, F.",
+	title="Generating Calligraphic Trajectories with Model Predictive Control",
+	booktitle="Proc. 43rd Conf. on Graphics Interface",
+	year="2017",
+	month="May",
+	address="Edmonton, AL, Canada",
+	pages=""
+}
+```
+
+[9] Adaptive assistance:
+```
+@article{Pignat17RAS,
+	author="Pignat, E. and Calinon, S.",
+	title="Learning adaptive dressing assistance from human demonstration",
+	journal="Robotics and Autonomous Systems",
+	year="2017",
+	month="July",
+	volume="93",
+	number="",
+	pages="61--75",
+	doi="10.1016/j.robot.2017.03.017",
+}
+```
+
+### List of examples
+
+This project will build a number of executables in the 'examples' folder, as listed in the table below.
+
+| Filename | ref. | Description |
+|----------|------|-------------|
+| test_adhsmm.cpp | [2] | Computation of the forward variable of an ADHSMM for a given external input that modifies the duration probabilities of the model |
+| test_gmm.cpp | [1] | Gaussian mixture model (GMM) |
+| test_gmr*.cpp | [1] | Gaussian mixture regression (GMR) |
+| test_hsmm*.cpp | [2] | Hidden semi-Markov model (HSMM) | 
+| test_lqr.cpp | [1] | Linear quadratic regulator (LQR) with either finite or infinite horizons, continuous version, iterative computation | 
+| test_lqr_discrete.cpp | [1] | Discrete version of LQR |
+| test_mpc.cpp | [1] | Model predictive control (MPC), batch LQR computation |
+| test_onlineDP.cpp  | [6] | DP-means for online estimation of GMM parameters | 
+| test_quaternions.cpp  | [3] | Orientations as unit quaternions |
+| test_rewardWeightedRefinement.cpp  | [] | EM-based stochasitc optimization |
+| test_tpgmm*.cpp  | [1] | Task-parameterized Gaussian mixture model (TP-GMM) | 
+| test_tphsmm.cpp  | [1,2] | Task-parameterized hidden semi-Markov model (TP-HSMM) |
+| test_trajGMM.cpp  | [1] | GMM with dynamic features (trajectory GMM) | 
+| test_trajMPC*.cpp  | [] | Generation of trajectory distributions with MPC | 
+
+

examples/test_dmp.cpp

-/**
-Copyright (C) 2014, Davide De Tommaso, Milad Malekzadeh, Sylvain Calinon
-
-This file is part of PbDLib.
-
-    PbDLib is free software: you can redistribute it and/or modify
-    it under the terms of the GNU Lesser General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    PbDLib is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU Lesser General Public License for more details.
-
-    You should have received a copy of the GNU Lesser General Public License
-    along with PbDLib.  If not, see <http://www.gnu.org/licenses/>.
-*/
-
-/*! \file repro_gmr.cpp
-\brief Learning GMM model and testing reproductions with GMR
-Learning a GMM model from a demonstration saved in the file data_txyz.txt and after
-
-\author Davide De Tommaso, Milad Malekzadeh, Sylvain Calinon
-\bug No known bugs.
-*/
-
-#include "pbdlib/gmm.h"
-#include "pbdlib/gmr.h"
-#include <sstream>
-
-#define nbStates 3
-#define nbVar 4
-#define nbData 200
-
-using namespace pbdlib;
-
-int main(int argc, char **argv)
-{
-	
-  return 0;
-}
-