From 9b9cf0a1eb95c864b8f4a0dc8254786cc75968c1 Mon Sep 17 00:00:00 2001
From: scalinon <sylvain.calinon@idiap.ch>
Date: Sat, 6 Mar 2021 09:56:17 +0100
Subject: [PATCH] Comments removed
---
demos/demo_OC_DDP_humanoid01.m | 1 +
demos/demo_ergodicControl_2D01.m | 9 +++------
2 files changed, 4 insertions(+), 6 deletions(-)
diff --git a/demos/demo_OC_DDP_humanoid01.m b/demos/demo_OC_DDP_humanoid01.m
index 24ae72f..9f38315 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 17c4342..4bbad65 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
--
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