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Merge pull request #2388 from jcarpent/topic/examples
Restructure CppAD and CasADi examples
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,27 @@ | ||
| # | ||
| # Copyright (c) 2024 INRIA | ||
| # | ||
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| function(ADD_PINOCCHIO_CPP_CASADI_EXAMPLE EXAMPLE) | ||
| add_pinocchio_cpp_example(${EXAMPLE} CASADI) | ||
| endfunction() | ||
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| if(BUILD_WITH_CASADI_SUPPORT) | ||
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| add_pinocchio_cpp_casadi_example(casadi-aba) | ||
| add_pinocchio_cpp_casadi_example(casadi-rnea) | ||
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| if(BUILD_PYTHON_INTERFACE) | ||
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| set(${PROJECT_NAME}_CASADI_PYTHON_EXAMPLES quadrotor-ocp) | ||
| if(BUILD_WITH_COLLISION_SUPPORT) | ||
| list(APPEND ${PROJECT_NAME}_CASADI_PYTHON_EXAMPLES cartpole) | ||
| endif() | ||
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| foreach(EXAMPLE ${${PROJECT_NAME}_CASADI_PYTHON_EXAMPLES}) | ||
| set(EXAMPLE_NAME "${PROJECT_NAME}-example-py-casadi-${EXAMPLE}") | ||
| add_python_unit_test(${EXAMPLE_NAME} "examples/casadi/${EXAMPLE}.py" "bindings/python") | ||
| add_windows_dll_path_to_test(${EXAMPLE_NAME}) | ||
| endforeach() | ||
| endif() | ||
| endif() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,84 @@ | ||
| #include "pinocchio/autodiff/casadi.hpp" | ||
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| #include "pinocchio/multibody/sample-models.hpp" | ||
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| #include "pinocchio/algorithm/aba.hpp" | ||
| #include "pinocchio/algorithm/joint-configuration.hpp" | ||
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| int main(int /*argc*/, char ** /*argv*/) | ||
| { | ||
| using namespace pinocchio; | ||
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| typedef double Scalar; | ||
| typedef ::casadi::SX ADScalar; | ||
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| typedef ModelTpl<Scalar> Model; | ||
| typedef Model::Data Data; | ||
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| typedef ModelTpl<ADScalar> ADModel; | ||
| typedef ADModel::Data ADData; | ||
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| // Create a random humanoid model | ||
| Model model; | ||
| buildModels::humanoidRandom(model); | ||
| model.lowerPositionLimit.head<3>().fill(-1.); | ||
| model.upperPositionLimit.head<3>().fill(1.); | ||
| Data data(model); | ||
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| // Pick up random configuration, velocity and acceleration vectors. | ||
| Eigen::VectorXd q(model.nq); | ||
| q = randomConfiguration(model); | ||
| Eigen::VectorXd v(Eigen::VectorXd::Random(model.nv)); | ||
| Eigen::VectorXd tau(Eigen::VectorXd::Random(model.nv)); | ||
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| // Create CasADi model and data from model | ||
| typedef ADModel::ConfigVectorType ConfigVectorAD; | ||
| typedef ADModel::TangentVectorType TangentVectorAD; | ||
| ADModel ad_model = model.cast<ADScalar>(); | ||
| ADData ad_data(ad_model); | ||
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| // Create symbolic CasADi vectors | ||
| ::casadi::SX cs_q = ::casadi::SX::sym("q", model.nq); | ||
| ConfigVectorAD q_ad(model.nq); | ||
| q_ad = Eigen::Map<ConfigVectorAD>(static_cast<std::vector<ADScalar>>(cs_q).data(), model.nq, 1); | ||
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| ::casadi::SX cs_v = ::casadi::SX::sym("v", model.nv); | ||
| TangentVectorAD v_ad(model.nv); | ||
| v_ad = Eigen::Map<TangentVectorAD>(static_cast<std::vector<ADScalar>>(cs_v).data(), model.nv, 1); | ||
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| ::casadi::SX cs_tau = ::casadi::SX::sym("tau", model.nv); | ||
| TangentVectorAD tau_ad(model.nv); | ||
| tau_ad = | ||
| Eigen::Map<TangentVectorAD>(static_cast<std::vector<ADScalar>>(cs_tau).data(), model.nv, 1); | ||
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| // Build CasADi function | ||
| aba(ad_model, ad_data, q_ad, v_ad, tau_ad); | ||
| ::casadi::SX a_ad(model.nv, 1); | ||
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| for (Eigen::DenseIndex k = 0; k < model.nv; ++k) | ||
| a_ad(k) = ad_data.ddq[k]; | ||
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| ::casadi::Function eval_aba( | ||
| "eval_aba", ::casadi::SXVector{cs_q, cs_v, cs_tau}, ::casadi::SXVector{a_ad}); | ||
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| // Evaluate CasADi expression with real value | ||
| std::vector<double> q_vec((size_t)model.nq); | ||
| Eigen::Map<Eigen::VectorXd>(q_vec.data(), model.nq, 1) = q; | ||
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| std::vector<double> v_vec((size_t)model.nv); | ||
| Eigen::Map<Eigen::VectorXd>(v_vec.data(), model.nv, 1) = v; | ||
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| std::vector<double> tau_vec((size_t)model.nv); | ||
| Eigen::Map<Eigen::VectorXd>(tau_vec.data(), model.nv, 1) = tau; | ||
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| ::casadi::DM a_casadi_res = eval_aba(::casadi::DMVector{q_vec, v_vec, tau_vec})[0]; | ||
| Data::TangentVectorType a_casadi_vec = Eigen::Map<Data::TangentVectorType>( | ||
| static_cast<std::vector<double>>(a_casadi_res).data(), model.nv, 1); | ||
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| // Eval ABA using classic Pinocchio model | ||
| pinocchio::aba(model, data, q, v, tau); | ||
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| // Print both results | ||
| std::cout << "pinocchio double:\n" << "\ta = " << data.ddq.transpose() << std::endl; | ||
| std::cout << "pinocchio CasADi:\n" << "\ta = " << a_casadi_vec.transpose() << std::endl; | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,83 @@ | ||
| #include "pinocchio/autodiff/casadi.hpp" | ||
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| #include "pinocchio/multibody/sample-models.hpp" | ||
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| #include "pinocchio/algorithm/rnea.hpp" | ||
| #include "pinocchio/algorithm/joint-configuration.hpp" | ||
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| int main(int /*argc*/, char ** /*argv*/) | ||
| { | ||
| using namespace pinocchio; | ||
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| typedef double Scalar; | ||
| typedef ::casadi::SX ADScalar; | ||
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| typedef ModelTpl<Scalar> Model; | ||
| typedef Model::Data Data; | ||
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| typedef ModelTpl<ADScalar> ADModel; | ||
| typedef ADModel::Data ADData; | ||
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| // Create a random humanoid model | ||
| Model model; | ||
| buildModels::humanoidRandom(model); | ||
| model.lowerPositionLimit.head<3>().fill(-1.); | ||
| model.upperPositionLimit.head<3>().fill(1.); | ||
| Data data(model); | ||
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| // Pick up random configuration, velocity and acceleration vectors. | ||
| Eigen::VectorXd q(model.nq); | ||
| q = randomConfiguration(model); | ||
| Eigen::VectorXd v(Eigen::VectorXd::Random(model.nv)); | ||
| Eigen::VectorXd a(Eigen::VectorXd::Random(model.nv)); | ||
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| // Create CasADi model and data from model | ||
| typedef ADModel::ConfigVectorType ConfigVectorAD; | ||
| typedef ADModel::TangentVectorType TangentVectorAD; | ||
| ADModel ad_model = model.cast<ADScalar>(); | ||
| ADData ad_data(ad_model); | ||
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| // Create symbolic CasADi vectors | ||
| ::casadi::SX cs_q = ::casadi::SX::sym("q", model.nq); | ||
| ConfigVectorAD q_ad(model.nq); | ||
| q_ad = Eigen::Map<ConfigVectorAD>(static_cast<std::vector<ADScalar>>(cs_q).data(), model.nq, 1); | ||
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| ::casadi::SX cs_v = ::casadi::SX::sym("v", model.nv); | ||
| TangentVectorAD v_ad(model.nv); | ||
| v_ad = Eigen::Map<TangentVectorAD>(static_cast<std::vector<ADScalar>>(cs_v).data(), model.nv, 1); | ||
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| ::casadi::SX cs_a = ::casadi::SX::sym("a", model.nv); | ||
| TangentVectorAD a_ad(model.nv); | ||
| a_ad = Eigen::Map<TangentVectorAD>(static_cast<std::vector<ADScalar>>(cs_a).data(), model.nv, 1); | ||
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| // Build CasADi function | ||
| rnea(ad_model, ad_data, q_ad, v_ad, a_ad); | ||
| ::casadi::SX tau_ad(model.nv, 1); | ||
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| for (Eigen::DenseIndex k = 0; k < model.nv; ++k) | ||
| tau_ad(k) = ad_data.tau[k]; | ||
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| ::casadi::Function eval_rnea( | ||
| "eval_rnea", ::casadi::SXVector{cs_q, cs_v, cs_a}, ::casadi::SXVector{tau_ad}); | ||
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| // Evaluate CasADi expression with real value | ||
| std::vector<double> q_vec((size_t)model.nq); | ||
| Eigen::Map<Eigen::VectorXd>(q_vec.data(), model.nq, 1) = q; | ||
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| std::vector<double> v_vec((size_t)model.nv); | ||
| Eigen::Map<Eigen::VectorXd>(v_vec.data(), model.nv, 1) = v; | ||
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| std::vector<double> a_vec((size_t)model.nv); | ||
| Eigen::Map<Eigen::VectorXd>(a_vec.data(), model.nv, 1) = a; | ||
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| ::casadi::DM tau_casadi_res = eval_rnea(::casadi::DMVector{q_vec, v_vec, a_vec})[0]; | ||
| Data::TangentVectorType tau_casadi_vec = Eigen::Map<Data::TangentVectorType>( | ||
| static_cast<std::vector<double>>(tau_casadi_res).data(), model.nv, 1); | ||
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| // Eval RNEA using classic Pinocchio model | ||
| pinocchio::rnea(model, data, q, v, a); | ||
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| // Print both results | ||
| std::cout << "pinocchio double:\n" << "\ttau = " << data.tau.transpose() << std::endl; | ||
| std::cout << "pinocchio CasADi:\n" << "\ttau = " << tau_casadi_vec.transpose() << std::endl; | ||
| } |
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