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/////////////////////////////////////////////////////////////////////////////// |
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// BSD 3-Clause License |
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// |
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// Copyright (C) 2021-2025, University of Edinburgh, Heriot-Watt University |
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// Copyright note valid unless otherwise stated in individual files. |
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// All rights reserved. |
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/////////////////////////////////////////////////////////////////////////////// |
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#define BOOST_TEST_NO_MAIN |
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#define BOOST_TEST_ALTERNATIVE_INIT_API |
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#include "factory/impulse_cost.hpp" |
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#include "unittest_common.hpp" |
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using namespace boost::unit_test; |
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using namespace crocoddyl::unittest; |
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//----------------------------------------------------------------------------// |
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void test_partial_derivatives_against_impulse_numdiff( |
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ImpulseCostModelTypes::Type cost_type, PinocchioModelTypes::Type model_type, |
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ActivationModelTypes::Type activation_type) { |
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// create the model |
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const std::shared_ptr<crocoddyl::ActionModelAbstract>& model = |
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ImpulseCostModelFactory().create(cost_type, model_type, activation_type); |
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// create the corresponding data object and set the cost to nan |
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const std::shared_ptr<crocoddyl::ActionDataAbstract>& data = |
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model->createData(); |
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crocoddyl::ActionModelNumDiff model_num_diff(model); |
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const std::shared_ptr<crocoddyl::ActionDataAbstract>& data_num_diff = |
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model_num_diff.createData(); |
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// Generating random values for the state and control |
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Eigen::VectorXd x = model->get_state()->rand(); |
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const Eigen::VectorXd u = Eigen::VectorXd::Random(model->get_nu()); |
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// Computing the action derivatives |
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model->calc(data, x, u); |
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model->calcDiff(data, x, u); |
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model_num_diff.calc(data_num_diff, x, u); |
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model_num_diff.calcDiff(data_num_diff, x, u); |
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// Tolerance defined as in |
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// http://www.it.uom.gr/teaching/linearalgebra/NumericalRecipiesInC/c5-7.pdf |
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double tol = std::pow(model_num_diff.get_disturbance(), 1. / 3.); |
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BOOST_CHECK((data->Lx - data_num_diff->Lx).isZero(tol)); |
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BOOST_CHECK((data->Lu - data_num_diff->Lu).isZero(tol)); |
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if (model_num_diff.get_with_gauss_approx()) { |
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BOOST_CHECK((data->Lxx - data_num_diff->Lxx).isZero(tol)); |
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BOOST_CHECK((data->Lxu - data_num_diff->Lxu).isZero(tol)); |
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BOOST_CHECK((data->Luu - data_num_diff->Luu).isZero(tol)); |
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} |
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// Computing the action derivatives |
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x = model->get_state()->rand(); |
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model->calc(data, x); |
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model->calcDiff(data, x); |
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model_num_diff.calc(data_num_diff, x); |
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model_num_diff.calcDiff(data_num_diff, x); |
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// Checking the partial derivatives against numerical differentiation |
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BOOST_CHECK((data->Lx - data_num_diff->Lx).isZero(tol)); |
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if (model_num_diff.get_with_gauss_approx()) { |
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BOOST_CHECK((data->Lxx - data_num_diff->Lxx).isZero(tol)); |
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} |
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// Checking that casted computation is the same |
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#ifdef NDEBUG // Run only in release mode |
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const std::shared_ptr<crocoddyl::ActionModelAbstractTpl<float>>& |
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casted_model = model->cast<float>(); |
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const std::shared_ptr<crocoddyl::ActionDataAbstractTpl<float>>& casted_data = |
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casted_model->createData(); |
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Eigen::VectorXf x_f = x.cast<float>(); |
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const Eigen::VectorXf u_f = u.cast<float>(); |
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model->calc(data, x, u); |
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model->calcDiff(data, x, u); |
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casted_model->calc(casted_data, x_f, u_f); |
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casted_model->calcDiff(casted_data, x_f, u_f); |
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float tol_f = 80.f * std::sqrt(2.0f * std::numeric_limits<float>::epsilon()); |
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BOOST_CHECK((data->Lx.cast<float>() - casted_data->Lx).isZero(tol_f)); |
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BOOST_CHECK((data->Lu.cast<float>() - casted_data->Lu).isZero(tol_f)); |
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BOOST_CHECK((data->Lxx.cast<float>() - casted_data->Lxx).isZero(tol_f)); |
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BOOST_CHECK((data->Lxu.cast<float>() - casted_data->Lxu).isZero(tol_f)); |
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BOOST_CHECK((data->Luu.cast<float>() - casted_data->Luu).isZero(tol_f)); |
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model->calc(data, x); |
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model->calcDiff(data, x); |
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casted_model->calc(casted_data, x_f); |
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casted_model->calcDiff(casted_data, x_f); |
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BOOST_CHECK((data->Lx.cast<float>() - casted_data->Lx).isZero(tol_f)); |
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BOOST_CHECK((data->Lxx.cast<float>() - casted_data->Lxx).isZero(tol_f)); |
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#endif |
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} |
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//----------------------------------------------------------------------------// |
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void register_impulse_cost_model_unit_tests( |
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ImpulseCostModelTypes::Type cost_type, PinocchioModelTypes::Type model_type, |
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ActivationModelTypes::Type activation_type) { |
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boost::test_tools::output_test_stream test_name; |
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test_name << "test_" << cost_type << "_" << activation_type << "_" |
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<< model_type; |
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std::cout << "Running " << test_name.str() << std::endl; |
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test_suite* ts = BOOST_TEST_SUITE(test_name.str()); |
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ts->add(BOOST_TEST_CASE( |
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boost::bind(&test_partial_derivatives_against_impulse_numdiff, cost_type, |
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model_type, activation_type))); |
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framework::master_test_suite().add(ts); |
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} |
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bool init_function() { |
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// Test all the impulse cost model. Note that we can do it only with humanoids |
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// as it needs to test the contact wrench cone |
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for (std::size_t cost_type = 0; cost_type < ImpulseCostModelTypes::all.size(); |
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++cost_type) { |
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for (std::size_t activation_type = 0; |
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activation_type < |
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ActivationModelTypes::ActivationModelQuadraticBarrier; |
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++activation_type) { |
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register_impulse_cost_model_unit_tests( |
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ImpulseCostModelTypes::all[cost_type], PinocchioModelTypes::Talos, |
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ActivationModelTypes::all[activation_type]); |
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if (ImpulseCostModelTypes::all[cost_type] == |
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ImpulseCostModelTypes::CostModelResidualContactForce || |
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ImpulseCostModelTypes::all[cost_type] == |
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ImpulseCostModelTypes::CostModelResidualContactFrictionCone) { |
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register_impulse_cost_model_unit_tests( |
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ImpulseCostModelTypes::all[cost_type], PinocchioModelTypes::HyQ, |
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ActivationModelTypes::all[activation_type]); |
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} |
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} |
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} |
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return true; |
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} |
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int main(int argc, char** argv) { |
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return ::boost::unit_test::unit_test_main(&init_function, argc, argv); |
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} |
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