Tab spacing (2 chars).

README.md

-# pbdlib-matlab
-
-### Compatibility
-
-	The codes have been tested 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"
-	}
-
+# pbdlib-matlab
+
+### Compatibility
+
+	The codes have been tested 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"
+	}
+

productTPGMM.m

@@ -7,16 +7,16 @@ function [Mu, Sigma] = productTPGMM(model, p)
 % TP-GMM products 
 %See Eq. (6.0.5), (6.0.6) and (6.0.7) in doc/TechnicalReport.pdf
 for i = 1:model.nbStates
-  % Reallocating  
-  SigmaTmp = zeros(model.nbVar);
-  MuTmp = zeros(model.nbVar,1);
-  % Product of Gaussians
-  for m = 1 : model.nbFrames 
-    MuP = p(m).A * model.Mu(:,m,i) + p(m).b; 
-    SigmaP = p(m).A * model.Sigma(:,:,m,i) * p(m).A'; 
-    SigmaTmp = SigmaTmp + inv(SigmaP);
-    MuTmp = MuTmp + SigmaP\MuP; 
-  end
-  Sigma(:,:,i) = inv(SigmaTmp);
-  Mu(:,i) = Sigma(:,:,i) * MuTmp;    
+	% Reallocating  
+	SigmaTmp = zeros(model.nbVar);
+	MuTmp = zeros(model.nbVar,1);
+	% Product of Gaussians
+	for m = 1 : model.nbFrames 
+		MuP = p(m).A * model.Mu(:,m,i) + p(m).b; 
+		SigmaP = p(m).A * model.Sigma(:,:,m,i) * p(m).A'; 
+		SigmaTmp = SigmaTmp + inv(SigmaP);
+		MuTmp = MuTmp + SigmaP\MuP; 
+	end
+	Sigma(:,:,i) = inv(SigmaTmp);
+	Mu(:,i) = Sigma(:,:,i) * MuTmp;    
 end

reproduction_LQR_infiniteHorizon.m

@@ -35,49 +35,49 @@ R = eye(nbVarOut) * rFactor;
 tar = [r.currTar; zeros(nbVarOut,nbData)];
 dtar = gradient(tar,1,2)/model.dt;
 
-  
+	
 %% Reproduction with varying impedance parameters
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 x = currPos;
 dx = zeros(nbVarOut,1);
 for t=1:nbData
 	%Current weighting term
-  Q(1:nbVarOut,1:nbVarOut) = inv(r.currSigma(:,:,t)); 
-  
-  %care() is a function from the Matlab Control Toolbox to solve the algebraic Riccati equation, 
+	Q(1:nbVarOut,1:nbVarOut) = inv(r.currSigma(:,:,t)); 
+	
+	%care() is a function from the Matlab Control Toolbox to solve the algebraic Riccati equation, 
 	%also available with the control toolbox of GNU Octave
-  %P = care(A, B, (Q+Q')/2, R); %(Q+Q')/2 is used instead of Q to avoid warnings for the symmetry of Q 
-  
-  %Alternatively, the function below can be used for an implementation based on Schur decomposition
-  %P = solveAlgebraicRiccati_Schur(A, B/R*B', (Q+Q')/2);
+	%P = care(A, B, (Q+Q')/2, R); %(Q+Q')/2 is used instead of Q to avoid warnings for the symmetry of Q 
+	
+	%Alternatively, the function below can be used for an implementation based on Schur decomposition
+	%P = solveAlgebraicRiccati_Schur(A, B/R*B', (Q+Q')/2);
 	
 	%Alternatively, the function below can be used for an implementation based on Eigendecomposition
 	P = solveAlgebraicRiccati_eig(A, B/R*B', (Q+Q')/2); %See Eq. (5.1.27) and Sec. (5.2) in doc/TechnicalReport.pdf
-  
-  %Variable for feedforward term computation (optional for movements with low dynamics)
-  d = (P*B*(R\B')-A') \ (P*dtar(:,t) - P*A*tar(:,t)); %See Eq. (5.1.28) in doc/TechnicalReport.pdf
-  
-  %Feedback term
-  L = R\B'*P; %See Eq. (5.1.30) in doc/TechnicalReport.pdf
-  %Feedforward term
-  M = R\B'*d; %See Eq. (5.1.30) in doc/TechnicalReport.pdf
-  
-  %Compute acceleration (with only feedback terms)
-  %ddx =  -L * [x-r.currTar(:,t); dx];
-  
-  %Compute acceleration (with feedback and feedforward terms)
-  ddx =  -L * [x-r.currTar(:,t); dx] + M; %See Eq. (5.1.30) in doc/TechnicalReport.pdf
-  r.FB(:,t) = -L * [x-r.currTar(:,t); dx];
-  r.FF(:,t) = M;
-  
-  %Update velocity and position
-  dx = dx + ddx * model.dt;
-  x = x + dx * model.dt;
-  %Log data
-  r.Data(:,t) = [DataIn(:,t); x]; 
-  r.ddxNorm(t) = norm(ddx);
-  r.kpDet(t) = det(L(:,1:nbVarOut));
-  r.kvDet(t) = det(L(:,nbVarOut+1:end));
+	
+	%Variable for feedforward term computation (optional for movements with low dynamics)
+	d = (P*B*(R\B')-A') \ (P*dtar(:,t) - P*A*tar(:,t)); %See Eq. (5.1.28) in doc/TechnicalReport.pdf
+	
+	%Feedback term
+	L = R\B'*P; %See Eq. (5.1.30) in doc/TechnicalReport.pdf
+	%Feedforward term
+	M = R\B'*d; %See Eq. (5.1.30) in doc/TechnicalReport.pdf
+	
+	%Compute acceleration (with only feedback terms)
+	%ddx =  -L * [x-r.currTar(:,t); dx];
+	
+	%Compute acceleration (with feedback and feedforward terms)
+	ddx =  -L * [x-r.currTar(:,t); dx] + M; %See Eq. (5.1.30) in doc/TechnicalReport.pdf
+	r.FB(:,t) = -L * [x-r.currTar(:,t); dx];
+	r.FF(:,t) = M;
+	
+	%Update velocity and position
+	dx = dx + ddx * model.dt;
+	x = x + dx * model.dt;
+	%Log data
+	r.Data(:,t) = [DataIn(:,t); x]; 
+	r.ddxNorm(t) = norm(ddx);
+	r.kpDet(t) = det(L(:,1:nbVarOut));
+	r.kvDet(t) = det(L(:,nbVarOut+1:end));
 end