diff --git a/demos/demo_OC_DDP_humanoid01.m b/demos/demo_OC_DDP_humanoid01.m
index 24ae72f4442c249f315f33229f791f8f39a49a7f..9f38315750df3422660c49091a904a0b52e72943 100644
--- a/demos/demo_OC_DDP_humanoid01.m
+++ b/demos/demo_OC_DDP_humanoid01.m
@@ -1,5 +1,6 @@
 function demo_OC_DDP_humanoid01
 % iLQR applied to a planar 5-link humanoid problem with constraints between joints.
+% (see also demo_OC_DDP_CoM01.m)
 %
 % If this code is useful for your research, please cite the related publication:
 % @article{Lembono21,
diff --git a/demos/demo_ergodicControl_2D01.m b/demos/demo_ergodicControl_2D01.m
index 17c4342be2cddd1a28a9bbba99cdfaed3d3c0d29..4bbad6583b0402729ebfff8ed7a9462242557d29 100644
--- a/demos/demo_ergodicControl_2D01.m
+++ b/demos/demo_ergodicControl_2D01.m
@@ -42,7 +42,7 @@ nbVar = 2; %Dimension of datapoint
 nbStates = 2; %Number of Gaussians to represent the spatial distribution
 sp = (nbVar + 1) / 2; %Sobolev norm parameter
 dt = 1E-2; %Time step
-xlim = [0; .5]; %Domain limit for each dimension (considered to be 1 for each dimension in this implementation)
+xlim = [0; 1]; %Domain limit for each dimension (considered to be 1 for each dimension in this implementation)
 L = (xlim(2) - xlim(1)) * 2; %Size of [-xlim(2),xlim(2)]
 om = 2 * pi / L; %omega
 u_max = 1E1; %Maximum speed allowed 
@@ -58,9 +58,6 @@ Sigma(:,:,1) = [.3;.1]*[.3;.1]' .*5E-1 + eye(nbVar)*5E-3; %eye(nbVar).*1E-2;
 Mu(:,2) =  [.6; .3]; 
 Sigma(:,:,2) = [.1;.2]*[.1;.2]' .*3E-1 + eye(nbVar)*1E-2;
 
-Mu = Mu * 0.5;
-Sigma = Sigma * 0.5;
-
 % Mu = rand(nbVar, nbStates);
 % Sigma = repmat(eye(nbVar)*1E-1, [1,1,nbStates]);
 
@@ -74,8 +71,8 @@ rg = 0:nbFct-1;
 [KX(1,:,:), KX(2,:,:)] = ndgrid(rg, rg);
 Lambda = (KX(1,:).^2 + KX(2,:).^2 + 1)'.^-sp; %Weighting vector (Eq.(15))
 
-%Explicit description of phi_k by exploiting the Fourier transform properties of Gaussians (optimized version by exploiting symmetries)
-%Enumerate symmetry operations for 2D signal ([-1,-1],[-1,1],[1,-1] and [1,1]), and removing redundant ones -> keeping ([-1,-1],[-1,1])
+%Explicit description of phi_k by exploiting the Fourier transform properties of Gaussians (optimized version by exploiting symmetries),
+%by enumerating symmetry operations for 2D signal ([-1,-1],[-1,1],[1,-1] and [1,1]), and removing redundant ones -> keeping ([-1,-1],[-1,1])
 op = hadamard(2^(nbVar-1));
 op = op(1:nbVar,:);
 %Compute phi_k