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Creation of tutorial examples

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README.md
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### Usage
Unzip the file and run 'demo_TPGMR_LQR01' (finite horizon LQR), 'demo_TPGMR_LQR02' (infinite horizon LQR) or
'demo_TPGMR_DS01' (dynamical system with constant gains) in Matlab.
'demo_testLQR01', 'demo_testLQR02' and 'demo_testLQR03' can also be run as additional examples of LQR.
Examples starting with 'demo_' can be run from Matlab/GNU Octave.
### Reference
### References
Calinon, S. (in preparation). A tutorial on task-parameterized movement learning and retrieval.
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
Demonstration of 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
......@@ -25,10 +25,10 @@
estimate the stiffness and damping feedback terms of the spring-damper systems, resulting in a minimal intervention
control strategy.
### Authors
### Contact
Sylvain Calinon and Danilo Bruno, 2014
http://programming-by-demonstration.org/
Sylvain Calinon
http://programming-by-demonstration.org/lib/
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:
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benchmark_DS_GP_GMM01.m 0 → 100644
View file @ 85dd5640
function benchmark_DS_GP_GMM01
%Benchmark of task-parameterized model based on Gaussian process regression,
%with trajectory model (Gaussian mixture model encoding), and DS-GMR used for reproduction
%Sylvain Calinon, 2015
addpath('./m_fcts/');
%% Parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
model.nbStates = 3; %Number of Gaussians in the GMM
model.nbFrames = 2; %Number of candidate frames of reference
model.nbVar = 3; %Dimension of the datapoints in the dataset (here: t,x1,x2)
model.dt = 0.01; %Time step
model.kP = 100; %Stiffness gain
model.kV = (2*model.kP)^.5; %Damping gain (with ideal underdamped damping ratio)
nbRepros = 4; %Number of reproductions with new situations randomly generated
nbVarOut = model.nbVar-1;
L = [eye(nbVarOut)*model.kP, eye(nbVarOut)*model.kV];
%% Load 3rd order tensor data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp('Load 3rd order tensor data...');
% The MAT file contains a structure 's' with the multiple demonstrations. 's(n).Data' is a matrix data for
% sample n (with 's(n).nbData' datapoints). 's(n).p(m).b' and 's(n).p(m).A' contain the position and
% orientation of the m-th candidate coordinate system for this demonstration. 'Data' contains the observations
% in the different frames. It is a 3rd order tensor of dimension D x P x N, with D=3 the dimension of a
% datapoint, P=2 the number of candidate frames, and N=200x4 the number of datapoints in a trajectory (200)
% multiplied by the number of demonstrations (5).
load('data/DataLQR01.mat');
%% Transformation of 'Data' to learn the path of the spring-damper system
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nbD = s(1).nbData;
nbVarOut = model.nbVar - 1;
%Create transformation matrix to compute [X; DX; DDX]
D = (diag(ones(1,nbD-1),-1)-eye(nbD)) / model.dt;
D(end,end) = 0;
%Create transformation matrix to compute XHAT = X + DX*kV/kP + DDX/kP
K1d = [1, model.kV/model.kP, 1/model.kP];
K = kron(K1d,eye(nbVarOut));
%Create 3rd order tensor data with XHAT instead of X, see Eq. (4.0.2) in doc/TechnicalReport.pdf
%Data = zeros(model.nbVar, model.nbFrames, nbD*nbSamples);
Data = s(1).Data0(1,:);
for n=1:nbSamples
DataTmp = s(n).Data0(2:end,:);
s(n).Data = K * [DataTmp; DataTmp*D; DataTmp*D*D];
Data = [Data; s(n).Data]; %Data is a matrix of size M*D x T (stacking the different trajectory samples)
end
%% GPR with GMM encoding
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fprintf('Parameters estimation of GPR with GMM encoding:');
%GMM encoding for each trajectory sample, learned at once by stacking the different trajectory samples in 'Data' matrix of size M*D x T
model.nbVar = size(Data,1); %Temporary modification of nbVar (for stacked data training)
model = init_GMM_timeBased(Data, model);
model = EM_GMM(Data, model);
model.nbVar = size(s(1).p(1).A,1); %Setting back the initial nbVar
for n=1:nbSamples
id = (n-1)*2+2:n*2+1;
s(n).Priors = model.Priors;
s(n).Mu = model.Mu([1,id],:);
s(n).Sigma = model.Sigma([1,id],[1,id],:);
%Regularization of Sigma
for i=1:model.nbStates
[V,D] = eig(s(n).Sigma(:,:,i));
U(:,:,i) = V * max(D,1E-3).^.5;
s(n).Sigma(:,:,i) = U(:,:,i) * U(:,:,i)';
end
%Set query point vector (position and orientation of the two objects)
s(n).DataIn = [s(n).p(1).b(2:3); s(n).p(1).A(2:3,3); s(n).p(2).b(2:3); s(n).p(2).A(2:3,3)];
model.DataIn(:,n) = s(n).DataIn;
%Set model output vector (Mu and Sigma)
model.DataOut(:,n) = [reshape(s(n).Mu, model.nbVar*model.nbStates, 1); reshape(s(n).Sigma, model.nbVar^2*model.nbStates, 1)];
%model.DataOut(:,n) = [reshape(s(n).Mu, model.nbVar*model.nbStates, 1); reshape(U, model.nbVar^2*model.nbStates, 1)];
end
%% Reproduction with GPR and DS-GMR for the task parameters used to train the model
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp('Reproductions with DS-GMR...');
DataIn = [1:s(1).nbData] * model.dt;
for n=1:nbSamples
%Rebuild model parameters with GPR
vOut = GPR(model.DataIn, model.DataOut, s(n).DataIn);
%Re-arrange GPR output as GMM parameters
r(n).Mu = reshape(vOut(1:model.nbVar*model.nbStates), model.nbVar, model.nbStates);
r(n).Sigma = reshape(vOut(model.nbVar*model.nbStates+1:end), model.nbVar, model.nbVar, model.nbStates);
% U = reshape(vOut(model.nbVar*model.nbStates+1:end), model.nbVar, model.nbVar, model.nbStates);
% for i=1:model.nbStates
% r(n).Sigma(:,:,i) = U(:,:,i) * U(:,:,i)';
% end
r(n).Priors = model.Priors;
r(n).nbStates = model.nbStates;
%Regularization of Sigma
for i=1:model.nbStates
[V,D] = eig(r(n).Sigma(:,:,i));
r(n).Sigma(:,:,i) = V * max(D,1E-3) * V';
end
% %Retrieval of attractor path through GMR
% currTar = GMR(r(n), DataIn, 1, [2:model.nbVar]); %See Eq. (3.0.2) to (3.0.5) in doc/TechnicalReport.pdf
%
% %Motion retrieval with spring-damper system
% x = s(n).p(1).b(2:model.nbVar);
% dx = zeros(nbVarOut,1);
% for t=1:s(n).nbData
% %Compute acceleration, velocity and position
% ddx = -L * [x-currTar(:,t); dx]; %See Eq. (4.0.1) in doc/TechnicalReport.pdf
% dx = dx + ddx * model.dt;
% x = x + dx * model.dt;
% r(n).Data(:,t) = x;
% end
end
%% Reproduction with GPR and DS-GMR for new task parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp('New reproductions with DS-GMR...');
load('data/taskParams.mat'); %Load new task parameters (new situation)
for n=1:nbRepros
rnew(n).p = taskParams(n).p;
%Query point vector (position and orientation of the two objects)
rnew(n).DataIn = [rnew(n).p(1).b(2:3); rnew(n).p(1).A(2:3,3); rnew(n).p(2).b(2:3); rnew(n).p(2).A(2:3,3)];
%Rebuild model parameters with GPR
vOut = GPR(model.DataIn, model.DataOut, rnew(n).DataIn);
%Re-arrange GPR output as GMM parameters
rnew(n).Mu = reshape(vOut(1:model.nbVar*model.nbStates), model.nbVar, model.nbStates);
rnew(n).Sigma = reshape(vOut(model.nbVar*model.nbStates+1:end), model.nbVar, model.nbVar, model.nbStates);
% U = reshape(vOut(model.nbVar*model.nbStates+1:end), model.nbVar, model.nbVar, model.nbStates);
% for i=1:model.nbStates
% rnew(n).Sigma(:,:,i) = U(:,:,i) * U(:,:,i)';
% end
rnew(n).Priors = model.Priors;
rnew(n).nbStates = model.nbStates;
%Regularization of Sigma
for i=1:model.nbStates
[V,D] = eig(rnew(n).Sigma(:,:,i));
rnew(n).Sigma(:,:,i) = V * max(D,1E-3) * V';
end
%Retrieval of attractor path through GMR
[rnew(n).currTar, rnew(n).currSigma] = GMR(rnew(n), DataIn, 1, [2:model.nbVar]); %See Eq. (3.0.2) to (3.0.5) in doc/TechnicalReport.pdf
%Motion retrieval with spring-damper system
x = rnew(n).p(1).b(2:model.nbVar);
dx = zeros(nbVarOut,1);
for t=1:nbD
%Compute acceleration, velocity and position
ddx = -L * [x-rnew(n).currTar(:,t); dx]; %See Eq. (4.0.1) in doc/TechnicalReport.pdf
dx = dx + ddx * model.dt;
x = x + dx * model.dt;
rnew(n).Data(:,t) = x;
end
end
%% Plots
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure('PaperPosition',[0 0 4 3],'position',[20,50,600,450]);
axes('Position',[0 0 1 1]); axis off; hold on;
set(0,'DefaultAxesLooseInset',[0,0,0,0]);
limAxes = [-1.5 2.5 -1.6 1.4]*.8;
myclr = [0.2863 0.0392 0.2392; 0.9137 0.4980 0.0078; 0.7412 0.0824 0.3137];
%Plot demonstrations
plotPegs(s(1).p(1), myclr(1,:), .1);
for n=1:nbSamples
plotPegs(s(n).p(2), myclr(2,:), .1);
patch([s(n).Data0(2,1:end) s(n).Data0(2,end:-1:1)], [s(n).Data0(3,1:end) s(n).Data0(3,end:-1:1)],...
[1 1 1],'linewidth',1.5,'edgecolor',[0 0 0],'facealpha',0,'edgealpha',0.04);
end
for n=1:nbSamples
plotGMM(r(n).Mu(2:3,:),r(n).Sigma(2:3,2:3,:), [0 0 0], .04);
end
axis equal; axis(limAxes);
print('-dpng','-r600','graphs/benchmark_DS_GP_GMM01.png');
%Plot reproductions in new situations
h=[];
for n=1:nbRepros
delete(h);
h = plotPegs(rnew(n).p);
h = [h plotGMM(rnew(n).currTar, rnew(n).currSigma, [0 .8 0], .2)];
h = [h plotGMM(rnew(n).Mu(2:3,:), rnew(n).Sigma(2:3,2:3,:), myclr(3,:), .6)];
h = [h patch([rnew(n).Data(1,:) rnew(n).Data(1,fliplr(1:nbD))], [rnew(n).Data(2,:) rnew(n).Data(2,fliplr(1:nbD))],...
[1 1 1],'linewidth',1.5,'edgecolor',[0 0 0],'facealpha',0,'edgealpha',0.4)];
h = [h plot(rnew(n).Data(1,1), rnew(n).Data(2,1),'.','markersize',12,'color',[0 0 0])];
axis equal; axis(limAxes);
print('-dpng','-r600',['graphs/benchmark_DS_GP_GMM' num2str(n+1,'%.2d') '.png']);
%pause
end
pause;
close all;
end
%Function to plot pegs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function h = plotPegs(p, colPegs, fa)
if ~exist('colPegs')
colPegs = [0.2863 0.0392 0.2392; 0.9137 0.4980 0.0078];
fa = 0.4;
end
pegMesh = [-4 -3.5; -4 10; -1.5 10; -1.5 -1; 1.5 -1; 1.5 10; 4 10; 4 -3.5; -4 -3.5]' *1E-1;
for m=1:length(p)
dispMesh = p(m).A(2:3,2:3) * pegMesh + repmat(p(m).b(2:3),1,size(pegMesh,2));
h(m) = patch(dispMesh(1,:),dispMesh(2,:),colPegs(m,:),'linewidth',1,'edgecolor','none','facealpha',fa);
end
end
benchmark_DS_GP_GMM02.m 0 → 100644
View file @ 85dd5640
function benchmark_DS_GP_GMM02
%Benchmark of task-parameterized model based on Gaussian process regression,
%with trajectory model (Gaussian mixture model encoding), and DS-GMR used for reproduction
%Sylvain Calinon, 2015
addpath('./m_fcts/');
%% Parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
model.nbStates = 3; %Number of Gaussians in the GMM
model.nbFrames = 2; %Number of candidate frames of reference
model.nbVar = 3; %Dimension of the datapoints in the dataset (here: t,x1,x2)
model.dt = 0.01; %Time step
model.kP = 100; %Stiffness gain
model.kV = (2*model.kP)^.5; %Damping gain (with ideal underdamped damping ratio)
nbRepros = 20; %Number of reproductions with new situations randomly generated
nbVarOut = model.nbVar-1;
L = [eye(nbVarOut)*model.kP, eye(nbVarOut)*model.kV];
%% Load 3rd order tensor data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp('Load 3rd order tensor data...');
% The MAT file contains a structure 's' with the multiple demonstrations. 's(n).Data' is a matrix data for
% sample n (with 's(n).nbData' datapoints). 's(n).p(m).b' and 's(n).p(m).A' contain the position and
% orientation of the m-th candidate coordinate system for this demonstration. 'Data' contains the observations
% in the different frames. It is a 3rd order tensor of dimension D x P x N, with D=3 the dimension of a
% datapoint, P=2 the number of candidate frames, and N=200x4 the number of datapoints in a trajectory (200)
% multiplied by the number of demonstrations (5).
load('data/DataLQR01.mat');
%% Transformation of 'Data' to learn the path of the spring-damper system
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nbD = s(1).nbData;
nbVarOut = model.nbVar - 1;
%Create transformation matrix to compute [X; DX; DDX]
D = (diag(ones(1,nbD-1),-1)-eye(nbD)) / model.dt;
D(end,end) = 0;
%Create transformation matrix to compute XHAT = X + DX*kV/kP + DDX/kP
K1d = [1, model.kV/model.kP, 1/model.kP];
K = kron(K1d,eye(nbVarOut));
%Create 3rd order tensor data with XHAT instead of X, see Eq. (4.0.2) in doc/TechnicalReport.pdf
%Data = zeros(model.nbVar, model.nbFrames, nbD*nbSamples);
Data = s(1).Data0(1,:);
for n=1:nbSamples
DataTmp = s(n).Data0(2:end,:);
s(n).Data = K * [DataTmp; DataTmp*D; DataTmp*D*D];
Data = [Data; s(n).Data]; %Data is a matrix of size M*D x T (stacking the different trajectory samples)
end
%% GPR with GMM encoding
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fprintf('Parameters estimation of GPR with GMM encoding:');
%GMM encoding for each trajectory sample, learned at once by stacking the different trajectory samples in 'Data' matrix of size M*D x T
model.nbVar = size(Data,1); %Temporary modification of nbVar (for stacked data training)
model = init_GMM_timeBased(Data, model);
model = EM_GMM(Data, model);
model.nbVar = size(s(1).p(1).A,1); %Setting back the initial nbVar
for n=1:nbSamples
id = (n-1)*2+2:n*2+1;
s(n).Priors = model.Priors;
s(n).Mu = model.Mu([1,id],:);
s(n).Sigma = model.Sigma([1,id],[1,id],:);
% %Regularization of Sigma
% for i=1:model.nbStates
% [V,D] = eig(s(n).Sigma(:,:,i));
% U(:,:,i) = V * max(D,1E-3).^.5;
% s(n).Sigma(:,:,i) = U(:,:,i) * U(:,:,i)';
% end
%Set query point vector (position and orientation of the two objects)
s(n).DataIn = [s(n).p(1).b(2:3); s(n).p(1).A(2:3,3); s(n).p(2).b(2:3); s(n).p(2).A(2:3,3)];
model.DataIn(:,n) = s(n).DataIn;
%Set model output vector (Mu and Sigma)
model.DataOut(:,n) = [reshape(s(n).Mu, model.nbVar*model.nbStates, 1); reshape(s(n).Sigma, model.nbVar^2*model.nbStates, 1)];
%model.DataOut(:,n) = [reshape(s(n).Mu, model.nbVar*model.nbStates, 1); reshape(U, model.nbVar^2*model.nbStates, 1)];
end
% %% Reproduction with GPR and DS-GMR for the task parameters used to train the model
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% disp('Reproductions with DS-GMR...');
% DataIn = [1:s(1).nbData] * model.dt;
% for n=1:nbSamples
% %Rebuild model parameters with GPR
% vOut = GPR(model.DataIn, model.DataOut, s(n).DataIn);
%
% %Re-arrange GPR output as GMM parameters
% r(n).Mu = reshape(vOut(1:model.nbVar*model.nbStates), model.nbVar, model.nbStates);
% r(n).Sigma = reshape(vOut(model.nbVar*model.nbStates+1:end), model.nbVar, model.nbVar, model.nbStates);
% % U = reshape(vOut(model.nbVar*model.nbStates+1:end), model.nbVar, model.nbVar, model.nbStates);
% % for i=1:model.nbStates
% % r(n).Sigma(:,:,i) = U(:,:,i) * U(:,:,i)';
% % end
% r(n).Priors = model.Priors;
% r(n).nbStates = model.nbStates;
%
% %Regularization of Sigma
% for i=1:model.nbStates
% [V,D] = eig(r(n).Sigma(:,:,i));
% r(n).Sigma(:,:,i) = V * max(D,1E-3) * V';
% end
%
% % %Retrieval of attractor path through GMR
% % currTar = GMR(r(n), DataIn, 1, [2:model.nbVar]); %See Eq. (3.0.2) to (3.0.5) in doc/TechnicalReport.pdf
% %
% % %Motion retrieval with spring-damper system
% % x = s(n).p(1).b(2:model.nbVar);
% % dx = zeros(nbVarOut,1);
% % for t=1:s(n).nbData
% % %Compute acceleration, velocity and position
% % ddx = -L * [x-currTar(:,t); dx]; %See Eq. (4.0.1) in doc/TechnicalReport.pdf
% % dx = dx + ddx * model.dt;
% % x = x + dx * model.dt;
% % r(n).Data(:,t) = x;
% % end
% end
%% Reproduction with GPR and DS-GMR for new task parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp('New reproductions with DS-GMR...');
% %Random generation of new task parameters
% for n=1:10
% id=ceil(rand(2,1)*nbSamples);
% w=rand(2); w=w/sum(w);
% taskParams(n).p(1) = s(1).p(1);
% taskParams(n).p(2).b = s(id(1)).p(2).b * w(1) + s(id(2)).p(2).b * w(2);
% taskParams(n).p(2).A = s(id(1)).p(2).A * w(1) + s(id(2)).p(2).A * w(2);
% end
% for n=11:20
% taskParams(n).p(1) = s(1).p(1);
% taskParams(n).p(2).b = [0; rand(1,1) * 2; rand(1,1)];
% aTmp = rand(1) * pi + pi;
% taskParams(n).p(2).A = [[1;0;0] [0;cos(aTmp);-sin(aTmp)] [0;sin(aTmp);cos(aTmp)]];
% taskParams(n).p(2).A(2:end,2:end) = taskParams(n).p(2).A(2:end,2:end) * norm(s(1).p(1).A(:,2));
% end
% save('data/taskParams3.mat','taskParams');
load('data/taskParams3.mat'); %Load new task parameters (new situation)
DataIn = [1:s(1).nbData] * model.dt;
for n=1:nbRepros
rnew(n).p = taskParams(n).p;
%Query point vector (position and orientation of the two objects)
rnew(n).DataIn = [rnew(n).p(1).b(2:3); rnew(n).p(1).A(2:3,3); rnew(n).p(2).b(2:3); rnew(n).p(2).A(2:3,3)];
%Rebuild model parameters with GPR
vOut = GPR(model.DataIn, model.DataOut, rnew(n).DataIn);
%Re-arrange GPR output as GMM parameters
rnew(n).Mu = reshape(vOut(1:model.nbVar*model.nbStates), model.nbVar, model.nbStates);
rnew(n).Sigma = reshape(vOut(model.nbVar*model.nbStates+1:end), model.nbVar, model.nbVar, model.nbStates);
% U = reshape(vOut(model.nbVar*model.nbStates+1:end), model.nbVar, model.nbVar, model.nbStates);
% for i=1:model.nbStates
% rnew(n).Sigma(:,:,i) = U(:,:,i) * U(:,:,i)';
% end
rnew(n).Priors = model.Priors;
rnew(n).nbStates = model.nbStates;
%Regularization of Sigma
for i=1:model.nbStates
[V,D] = eig(rnew(n).Sigma(:,:,i));
rnew(n).Sigma(:,:,i) = V * max(D,1E-3) * V';
end
%Retrieval of attractor path through GMR
[rnew(n).currTar, rnew(n).currSigma] = GMR(rnew(n), DataIn, 1, [2:model.nbVar]); %See Eq. (3.0.2) to (3.0.5) in doc/TechnicalReport.pdf
%Motion retrieval with spring-damper system
x = rnew(n).p(1).b(2:model.nbVar);
dx = zeros(nbVarOut,1);
for t=1:nbD
%Compute acceleration, velocity and position
ddx = -L * [x-rnew(n).currTar(:,t); dx]; %See Eq. (4.0.1) in doc/TechnicalReport.pdf
dx = dx + ddx * model.dt;
x = x + dx * model.dt;
rnew(n).Data(:,t) = x;
end
end
%% Plots
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure('PaperPosition',[0 0 4 3],'position',[20,50,600,450]);
axes('Position',[0 0 1 1]); axis off; hold on;
set(0,'DefaultAxesLooseInset',[0,0,0,0]);
limAxes = [-1.5 2.5 -1.6 1.4]*.8;
myclr = [0.2863 0.0392 0.2392; 0.9137 0.4980 0.0078; 0.7412 0.0824 0.3137];
%Plot step-by-step
plotPegs(s(1).p(1), myclr(1,:), .5);
for n=1:nbSamples
plotPegs(s(n).p(2), myclr(2,:), .1);
patch([s(n).Data0(2,1:end) s(n).Data0(2,end:-1:1)], [s(n).Data0(3,1:end) s(n).Data0(3,end:-1:1)],...
[1 1 1],'linewidth',1.5,'edgecolor',[0 0 0],'facealpha',0,'edgealpha',0.04);
end
axis equal; axis(limAxes);
h=[];
for n=1:nbSamples
delete(h)
h = plotPegs(s(n).p(2), myclr(2,:), .5);
h = [h plotGMM(s(n).Mu(2:3,:),s(n).Sigma(2:3,2:3,:), [0 0 0], 1)];
print('-dpng','-r600',['graphs/benchmark_DS_GP_GMM_intro_step' num2str(n) 'a.png']);
h = [h plot2DArrow(s(n).p(2).b(2:3), s(n).p(2).A(2:3,3), [.8 0 0])];
print('-dpng','-r600',['graphs/benchmark_DS_GP_GMM_intro_step' num2str(n) 'b.png']);
end
pause;
close all;
return;
%Plot demonstrations
plotPegs(s(1).p(1), myclr(1,:), .1);
for n=1:nbSamples
plotPegs(s(n).p(2), myclr(2,:), .1);
patch([s(n).Data0(2,1:end) s(n).Data0(2,end:-1:1)], [s(n).Data0(3,1:end) s(n).Data0(3,end:-1:1)],...
[1 1 1],'linewidth',1.5,'edgecolor',[0 0 0],'facealpha',0,'edgealpha',0.04);
end
for n=1:nbSamples
plotGMM(s(n).Mu(2:3,:),s(n).Sigma(2:3,2:3,:), [0 0 0], .04);
end
axis equal; axis(limAxes);
%print('-dpng','-r600',['graphs/benchmark_DS_GP_GMM_intro00.png']);
%Plot reproductions in new situations
h=[];
for n=1:nbRepros
delete(h);
h = plotPegs(rnew(n).p);
%h = [h plotGMM(rnew(n).currTar, rnew(n).currSigma, [0 .8 0], .2)];
%h = [h plotGMM(rnew(n).Mu(2:3,:), rnew(n).Sigma(2:3,2:3,:), myclr(3,:), .6)];
h = [h patch([rnew(n).Data(1,:) rnew(n).Data(1,fliplr(1:nbD))], [rnew(n).Data(2,:) rnew(n).Data(2,fliplr(1:nbD))],...
[1 1 1],'linewidth',1.5,'edgecolor',[0 0 0],'facealpha',0,'edgealpha',0.4)];
h = [h plot(rnew(n).Data(1,1), rnew(n).Data(2,1),'.','markersize',12,'color',[0 0 0])];
axis equal; axis(limAxes);
print('-dpng','-r600',['graphs/benchmark_DS_GP_GMM_intro' num2str(n,'%.2d') '.png']);
%pause
end
pause;
close all;
end
%Function to plot pegs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function h = plotPegs(p, colPegs, fa)
if ~exist('colPegs')
colPegs = [0.2863 0.0392 0.2392; 0.9137 0.4980 0.0078];
fa = 0.4;
end
pegMesh = [-4 -3.5; -4 10; -1.5 10; -1.5 -1; 1.5 -1; 1.5 10; 4 10; 4 -3.5; -4 -3.5]' *1E-1;
for m=1:length(p)
dispMesh = p(m).A(2:3,2:3) * pegMesh + repmat(p(m).b(2:3),1,size(pegMesh,2));
h(m) = patch(dispMesh(1,:),dispMesh(2,:),colPegs(m,:),'linewidth',1,'edgecolor','none','facealpha',fa);
end
end
benchmark_DS_GP_raw01.m 0 → 100644
View file @ 85dd5640
function benchmark_DS_GP_raw01
%Benchmark of task-parameterized model based on Gaussian process regression,
%with raw trajectory, and spring-damper system used for reproduction
%Sylvain Calinon, 2015
addpath('./m_fcts/');
%% Parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
model.nbFrames = 2; %Number of candidate frames of reference
model.nbVar = 2; %Dimension of the datapoints in the dataset (here: x1,x2)
model.dt = 0.01; %Time step
model.kP = 100; %Stiffness gain
model.kV = (2*model.kP)^.5; %Damping gain (with ideal underdamped damping ratio)
nbRepros = 4; %Number of reproductions with new situations randomly generated
L = [eye(model.nbVar)*model.kP, eye(model.nbVar)*model.kV];
%% Load 3rd order tensor data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp('Load 3rd order tensor data...');
% The MAT file contains a structure 's' with the multiple demonstrations. 's(n).Data' is a matrix data for
% sample n (with 's(n).nbData' datapoints). 's(n).p(m).b' and 's(n).p(m).A' contain the position and
% orientation of the m-th candidate coordinate system for this demonstration. 'Data' contains the observations
% in the different frames. It is a 3rd order tensor of dimension D x P x N, with D=3 the dimension of a
% datapoint, P=2 the number of candidate frames, and N=200x4 the number of datapoints in a trajectory (200)
% multiplied by the number of demonstrations (5).
load('data/DataLQR01.mat');
%% Transformation of 'Data' to learn the path of the spring-damper system
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nbD = s(1).nbData;
%Create transformation matrix to compute [X; DX; DDX]
D = (diag(ones(1,nbD-1),-1)-eye(nbD)) / model.dt;
D(end,end) = 0;
%Create transformation matrix to compute XHAT = X + DX*kV/kP + DDX/kP
K1d = [1, model.kV/model.kP, 1/model.kP];
K = kron(K1d,eye(model.nbVar));
%Create 3rd order tensor data with XHAT instead of X, see Eq. (4.0.2) in doc/TechnicalReport.pdf
%Data = zeros(model.nbVar, model.nbFrames, nbD*nbSamples);
Data = s(1).Data0(1,:);
for n=1:nbSamples
DataTmp = s(n).Data0(2:end,:);
s(n).Data = K * [DataTmp; DataTmp*D; DataTmp*D*D];
Data = [Data; s(n).Data]; %Data is a matrix of size M*D x T (stacking the different trajectory samples)
end
%% GPR with raw trajectory encoding
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fprintf('Parameters estimation of GPR with raw trajectory encoding:');
for n=1:nbSamples
%Set query point vector (position and orientation of the two objects)
s(n).DataIn = [s(n).p(1).b(2:3); s(n).p(1).A(2:3,3); s(n).p(2).b(2:3); s(n).p(2).A(2:3,3)];
model.DataIn(:,n) = s(n).DataIn;
%Set model output vector (raw trajectory data)
model.DataOut(:,n) = reshape(s(n).Data, model.nbVar*nbD, 1);
end
% %% Reproduction with GPR for the task parameters used to train the model
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% disp('Reproductions with spring-damper system...');
% for n=1:nbSamples