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benchmark_lfd_primp.m
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benchmark_lfd_primp.m
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% Benchmark script for PRIMP
%
% Author
% Sipu Ruan, 2023
close all; clear; clc;
add_paths()
% Name of the dataset
dataset_name = {'panda_arm', 'lasa_handwriting/pose_data'};
for j = 1:length(dataset_name)
% Name of demo types
demo_type = load_dataset_param(dataset_name{j});
for i = 1:length(demo_type)
disp('Benchmark: PRIMP (ours)')
disp(['Dataset: ', dataset_name{j}, ' (', num2str(j), '/', num2str(length(dataset_name)), ')'])
disp(['Demo type: ', demo_type{i}, ' (', num2str(i), '/', num2str(length(demo_type)), ')'])
% Run benchmark for each demo in each dataset
run_benchmark(dataset_name{j}, demo_type{i});
clc;
end
end
%% Run benchmark for each demo type
function run_benchmark(dataset_name, demo_type)
group_name = {'SE', 'PCG'};
data_folder = strcat("../data/", dataset_name, "/", demo_type, "/");
result_folder = strcat("../result/benchmark/", dataset_name, "/", demo_type, "/");
%% Load and parse demo data
argin.n_step = 50;
argin.data_folder = data_folder;
argin.group_name = 'SE';
filenames = dir(strcat(argin.data_folder, "*.json"));
g_demo = parse_demo_trajectory(filenames, argin);
% Load random configurations for conditioning
trials = load_random_trials(result_folder);
n_trial = length(trials.t_via{1});
%% Benchmark
res_via_1 = cell(n_trial, length(group_name));
res_via_2 = cell(n_trial, length(group_name));
for j = 1:length(group_name)
param.n_sample = 50;
param.group_name = group_name{j};
% Compute trajectory distribution from demonstrations
[g_mean, cov_t] = get_pdf_from_demo(g_demo, group_name{j});
for i = 1:n_trial
disp(['Group (', num2str(j), '/', num2str(length(group_name)),...
'): ', group_name{j}])
disp([num2str(i/(n_trial) * 100), '%'])
% Load random via-point poses
t_via_1 = trials.t_via{1}(i);
g_via_1 = trials.g_via{1}(:,:,i);
cov_via_1 = trials.cov_via{1}(:,:,i);
t_via_2 = trials.t_via{2}(i);
g_via_2 = trials.g_via{2}(:,:,i);
cov_via_2 = trials.cov_via{2}(:,:,i);
% Initiate class
res_via_1{i,j}.group_name = param.group_name;
res_via_2{i,j}.group_name = param.group_name;
t_start = tic;
primp_obj = PRIMP(g_mean.matrix, cov_t, param);
% Condition on via-point poses
primp_obj.get_condition_pdf(t_via_1, g_via_1, cov_via_1);
g_samples_1 = primp_obj.get_samples();
primp_obj.get_condition_pdf(t_via_2, g_via_2, cov_via_2);
g_samples_2 = primp_obj.get_samples();
t(i,j) = toc(t_start);
%% Distance to desired pose and original trajectory
% Convert to group structure
res_via_1{i,j} =...
generate_pose_struct(g_samples_1, param.group_name);
res_via_2{i,j} =...
generate_pose_struct(g_samples_2, param.group_name);
% Distance to demonstrated trajectories
d_demo.via_1(i,:,j) =...
evaluate_traj_distribution(res_via_1{i,j}, g_demo);
d_demo.via_2(i,:,j) =...
evaluate_traj_distribution(res_via_2{i,j}, g_demo);
% Distance to desired pose
d_via.via_1(i,:,j) =...
evaluate_desired_pose(res_via_1{i,j}, g_via_1, t_via_1);
d_via.via_2(i,:,j) =...
evaluate_desired_pose(res_via_2{i,j}, g_via_2, t_via_2);
end
end
%% Evaluation of benchmarks
% Store distance results
res_filename = strcat(result_folder, "result_lfd_primp.mat");
save(res_filename, "t", "d_demo", "d_via");
% Display and store command window
diary_filename = strcat(result_folder, "result_lfd_primp.txt");
if exist(diary_filename, 'file') ; delete(diary_filename); end
diary(diary_filename);
for j = 1:length(group_name)
disp('===============================================================')
disp(['Group: ', group_name{j}])
disp('>>>> Condition on 1 via point <<<<')
disp('---- Distance to demo (rot, tran):')
disp(num2str( mean(d_demo.via_1(:,:,j), 1) ))
disp('---- Distance to desired pose (rot, tran):')
disp(num2str( mean(d_via.via_1(:,:,j), 1) ))
disp('---------------------------------------------------------------')
disp('>>>> Condition on 2 via points <<<<')
disp('---- Distance to demo (rot, tran):')
disp(num2str( mean(d_demo.via_2(:,:,j), 1) ))
disp('---- Distance to desired pose (rot, tran):')
disp(num2str( mean(d_via.via_2(:,:,j), 1) ))
end
% Computational time
figure; hold on;
disp('>>>> Computation time <<<<')
disp(num2str( mean(t,1) ))
boxplot(t)
ylim([0, max(max(t))])
diary off
end