| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | /////////////////////////////////////////////////////////////////////////////// | ||
| 2 | // BSD 3-Clause License | ||
| 3 | // | ||
| 4 | // Copyright (C) 2021-2025, University of Edinburgh, Heriot-Watt University | ||
| 5 | // Copyright note valid unless otherwise stated in individual files. | ||
| 6 | // All rights reserved. | ||
| 7 | /////////////////////////////////////////////////////////////////////////////// | ||
| 8 | |||
| 9 | #define BOOST_TEST_NO_MAIN | ||
| 10 | #define BOOST_TEST_ALTERNATIVE_INIT_API | ||
| 11 | |||
| 12 | #include "factory/impulse_cost.hpp" | ||
| 13 | #include "unittest_common.hpp" | ||
| 14 | |||
| 15 | using namespace boost::unit_test; | ||
| 16 | using namespace crocoddyl::unittest; | ||
| 17 | |||
| 18 | //----------------------------------------------------------------------------// | ||
| 19 | |||
| 20 | ✗ | void test_partial_derivatives_against_impulse_numdiff( | |
| 21 | ImpulseCostModelTypes::Type cost_type, PinocchioModelTypes::Type model_type, | ||
| 22 | ActivationModelTypes::Type activation_type) { | ||
| 23 | // create the model | ||
| 24 | const std::shared_ptr<crocoddyl::ActionModelAbstract>& model = | ||
| 25 | ✗ | ImpulseCostModelFactory().create(cost_type, model_type, activation_type); | |
| 26 | |||
| 27 | // create the corresponding data object and set the cost to nan | ||
| 28 | const std::shared_ptr<crocoddyl::ActionDataAbstract>& data = | ||
| 29 | ✗ | model->createData(); | |
| 30 | |||
| 31 | ✗ | crocoddyl::ActionModelNumDiff model_num_diff(model); | |
| 32 | const std::shared_ptr<crocoddyl::ActionDataAbstract>& data_num_diff = | ||
| 33 | ✗ | model_num_diff.createData(); | |
| 34 | |||
| 35 | // Generating random values for the state and control | ||
| 36 | ✗ | Eigen::VectorXd x = model->get_state()->rand(); | |
| 37 | ✗ | const Eigen::VectorXd u = Eigen::VectorXd::Random(model->get_nu()); | |
| 38 | |||
| 39 | // Computing the action derivatives | ||
| 40 | ✗ | model->calc(data, x, u); | |
| 41 | ✗ | model->calcDiff(data, x, u); | |
| 42 | ✗ | model_num_diff.calc(data_num_diff, x, u); | |
| 43 | ✗ | model_num_diff.calcDiff(data_num_diff, x, u); | |
| 44 | // Tolerance defined as in | ||
| 45 | // http://www.it.uom.gr/teaching/linearalgebra/NumericalRecipiesInC/c5-7.pdf | ||
| 46 | ✗ | double tol = std::pow(model_num_diff.get_disturbance(), 1. / 3.); | |
| 47 | ✗ | BOOST_CHECK((data->Lx - data_num_diff->Lx).isZero(tol)); | |
| 48 | ✗ | BOOST_CHECK((data->Lu - data_num_diff->Lu).isZero(tol)); | |
| 49 | ✗ | if (model_num_diff.get_with_gauss_approx()) { | |
| 50 | ✗ | BOOST_CHECK((data->Lxx - data_num_diff->Lxx).isZero(tol)); | |
| 51 | ✗ | BOOST_CHECK((data->Lxu - data_num_diff->Lxu).isZero(tol)); | |
| 52 | ✗ | BOOST_CHECK((data->Luu - data_num_diff->Luu).isZero(tol)); | |
| 53 | } | ||
| 54 | |||
| 55 | // Computing the action derivatives | ||
| 56 | ✗ | x = model->get_state()->rand(); | |
| 57 | ✗ | model->calc(data, x); | |
| 58 | ✗ | model->calcDiff(data, x); | |
| 59 | ✗ | model_num_diff.calc(data_num_diff, x); | |
| 60 | ✗ | model_num_diff.calcDiff(data_num_diff, x); | |
| 61 | |||
| 62 | // Checking the partial derivatives against numerical differentiation | ||
| 63 | ✗ | BOOST_CHECK((data->Lx - data_num_diff->Lx).isZero(tol)); | |
| 64 | ✗ | if (model_num_diff.get_with_gauss_approx()) { | |
| 65 | ✗ | BOOST_CHECK((data->Lxx - data_num_diff->Lxx).isZero(tol)); | |
| 66 | } | ||
| 67 | |||
| 68 | // Checking that casted computation is the same | ||
| 69 | #ifdef NDEBUG // Run only in release mode | ||
| 70 | const std::shared_ptr<crocoddyl::ActionModelAbstractTpl<float>>& | ||
| 71 | casted_model = model->cast<float>(); | ||
| 72 | const std::shared_ptr<crocoddyl::ActionDataAbstractTpl<float>>& casted_data = | ||
| 73 | casted_model->createData(); | ||
| 74 | Eigen::VectorXf x_f = x.cast<float>(); | ||
| 75 | const Eigen::VectorXf u_f = u.cast<float>(); | ||
| 76 | model->calc(data, x, u); | ||
| 77 | model->calcDiff(data, x, u); | ||
| 78 | casted_model->calc(casted_data, x_f, u_f); | ||
| 79 | casted_model->calcDiff(casted_data, x_f, u_f); | ||
| 80 | float tol_f = 80.f * std::sqrt(2.0f * std::numeric_limits<float>::epsilon()); | ||
| 81 | BOOST_CHECK((data->Lx.cast<float>() - casted_data->Lx).isZero(tol_f)); | ||
| 82 | BOOST_CHECK((data->Lu.cast<float>() - casted_data->Lu).isZero(tol_f)); | ||
| 83 | BOOST_CHECK((data->Lxx.cast<float>() - casted_data->Lxx).isZero(tol_f)); | ||
| 84 | BOOST_CHECK((data->Lxu.cast<float>() - casted_data->Lxu).isZero(tol_f)); | ||
| 85 | BOOST_CHECK((data->Luu.cast<float>() - casted_data->Luu).isZero(tol_f)); | ||
| 86 | model->calc(data, x); | ||
| 87 | model->calcDiff(data, x); | ||
| 88 | casted_model->calc(casted_data, x_f); | ||
| 89 | casted_model->calcDiff(casted_data, x_f); | ||
| 90 | BOOST_CHECK((data->Lx.cast<float>() - casted_data->Lx).isZero(tol_f)); | ||
| 91 | BOOST_CHECK((data->Lxx.cast<float>() - casted_data->Lxx).isZero(tol_f)); | ||
| 92 | #endif | ||
| 93 | ✗ | } | |
| 94 | |||
| 95 | //----------------------------------------------------------------------------// | ||
| 96 | |||
| 97 | ✗ | void register_impulse_cost_model_unit_tests( | |
| 98 | ImpulseCostModelTypes::Type cost_type, PinocchioModelTypes::Type model_type, | ||
| 99 | ActivationModelTypes::Type activation_type) { | ||
| 100 | ✗ | boost::test_tools::output_test_stream test_name; | |
| 101 | ✗ | test_name << "test_" << cost_type << "_" << activation_type << "_" | |
| 102 | ✗ | << model_type; | |
| 103 | ✗ | std::cout << "Running " << test_name.str() << std::endl; | |
| 104 | ✗ | test_suite* ts = BOOST_TEST_SUITE(test_name.str()); | |
| 105 | ✗ | ts->add(BOOST_TEST_CASE( | |
| 106 | boost::bind(&test_partial_derivatives_against_impulse_numdiff, cost_type, | ||
| 107 | model_type, activation_type))); | ||
| 108 | ✗ | framework::master_test_suite().add(ts); | |
| 109 | ✗ | } | |
| 110 | |||
| 111 | ✗ | bool init_function() { | |
| 112 | // Test all the impulse cost model. Note that we can do it only with humanoids | ||
| 113 | // as it needs to test the contact wrench cone | ||
| 114 | ✗ | for (std::size_t cost_type = 0; cost_type < ImpulseCostModelTypes::all.size(); | |
| 115 | ++cost_type) { | ||
| 116 | ✗ | for (std::size_t activation_type = 0; | |
| 117 | ✗ | activation_type < | |
| 118 | ActivationModelTypes::ActivationModelQuadraticBarrier; | ||
| 119 | ++activation_type) { | ||
| 120 | ✗ | register_impulse_cost_model_unit_tests( | |
| 121 | ✗ | ImpulseCostModelTypes::all[cost_type], PinocchioModelTypes::Talos, | |
| 122 | ✗ | ActivationModelTypes::all[activation_type]); | |
| 123 | ✗ | if (ImpulseCostModelTypes::all[cost_type] == | |
| 124 | ✗ | ImpulseCostModelTypes::CostModelResidualContactForce || | |
| 125 | ✗ | ImpulseCostModelTypes::all[cost_type] == | |
| 126 | ImpulseCostModelTypes::CostModelResidualContactFrictionCone) { | ||
| 127 | ✗ | register_impulse_cost_model_unit_tests( | |
| 128 | ✗ | ImpulseCostModelTypes::all[cost_type], PinocchioModelTypes::HyQ, | |
| 129 | ✗ | ActivationModelTypes::all[activation_type]); | |
| 130 | } | ||
| 131 | } | ||
| 132 | } | ||
| 133 | |||
| 134 | ✗ | return true; | |
| 135 | } | ||
| 136 | |||
| 137 | ✗ | int main(int argc, char** argv) { | |
| 138 | ✗ | return ::boost::unit_test::unit_test_main(&init_function, argc, argv); | |
| 139 | } | ||
| 140 |