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/////////////////////////////////////////////////////////////////////////////// |
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// BSD 3-Clause License |
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// |
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// Copyright (C) 2019-2021, LAAS-CNRS, University of Edinburgh |
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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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#ifdef CROCODDYL_WITH_MULTITHREADING |
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#include <omp.h> |
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#endif |
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#ifndef CROCODDYL_WITH_NTHREADS |
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#define CROCODDYL_WITH_NTHREADS 1 |
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#endif |
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#ifdef CROCODDYL_WITH_CODEGEN |
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#include "crocoddyl/core/codegen/action-base.hpp" |
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#endif |
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#include "crocoddyl/core/solvers/fddp.hpp" |
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#include "crocoddyl/core/utils/file-io.hpp" |
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#include "crocoddyl/core/utils/timer.hpp" |
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#include "factory/arm-kinova.hpp" |
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#include "factory/arm.hpp" |
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#include "factory/legged-robots.hpp" |
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#define STDDEV(vec) \ |
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std::sqrt(((vec - vec.mean())).square().sum() / \ |
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(static_cast<double>(vec.size()) - 1)) |
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#define AVG(vec) (vec.mean()) |
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void print_benchmark(RobotEENames robot) { |
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unsigned int N = 100; // number of nodes |
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unsigned int T = 1e3; // number of trials |
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// Building the running and terminal models |
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boost::shared_ptr<crocoddyl::ActionModelAbstract> runningModel, terminalModel; |
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if (robot.robot_name == "Talos_arm") { |
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crocoddyl::benchmark::build_arm_action_models(runningModel, terminalModel); |
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} else if (robot.robot_name == "Kinova_arm") { |
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crocoddyl::benchmark::build_arm_kinova_action_models(runningModel, |
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terminalModel); |
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} else { |
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crocoddyl::benchmark::build_contact_action_models(robot, runningModel, |
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terminalModel); |
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} |
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// Get the initial state |
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boost::shared_ptr<crocoddyl::StateMultibody> state = |
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boost::static_pointer_cast<crocoddyl::StateMultibody>( |
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runningModel->get_state()); |
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std::cout << "NQ: " << state->get_nq() << std::endl; |
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std::cout << "Number of nodes: " << N << std::endl << std::endl; |
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Eigen::VectorXd default_state(state->get_nq() + state->get_nv()); |
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boost::shared_ptr<crocoddyl::IntegratedActionModelEulerTpl<double> > rm = |
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boost::static_pointer_cast< |
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crocoddyl::IntegratedActionModelEulerTpl<double> >(runningModel); |
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if (robot.robot_name == "Talos_arm" || robot.robot_name == "Kinova_arm") { |
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boost::shared_ptr< |
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crocoddyl::DifferentialActionModelFreeFwdDynamicsTpl<double> > |
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dm = boost::static_pointer_cast< |
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crocoddyl::DifferentialActionModelFreeFwdDynamicsTpl<double> >( |
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rm->get_differential()); |
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default_state |
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<< dm->get_pinocchio().referenceConfigurations[robot.reference_conf], |
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Eigen::VectorXd::Zero(state->get_nv()); |
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} else { |
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boost::shared_ptr< |
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crocoddyl::DifferentialActionModelContactFwdDynamicsTpl<double> > |
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dm = boost::static_pointer_cast< |
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crocoddyl::DifferentialActionModelContactFwdDynamicsTpl<double> >( |
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rm->get_differential()); |
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default_state |
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<< dm->get_pinocchio().referenceConfigurations[robot.reference_conf], |
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Eigen::VectorXd::Zero(state->get_nv()); |
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} |
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Eigen::VectorXd x0(default_state); |
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std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > runningModels( |
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N, runningModel); |
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boost::shared_ptr<crocoddyl::ShootingProblem> problem = |
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boost::make_shared<crocoddyl::ShootingProblem>(x0, runningModels, |
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terminalModel); |
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// Computing the warm-start |
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std::vector<Eigen::VectorXd> xs(N + 1, x0); |
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std::vector<Eigen::VectorXd> us( |
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N, Eigen::VectorXd::Zero(runningModel->get_nu())); |
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for (unsigned int i = 0; i < N; ++i) { |
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const boost::shared_ptr<crocoddyl::ActionModelAbstract>& model = |
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problem->get_runningModels()[i]; |
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const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data = |
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problem->get_runningDatas()[i]; |
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model->quasiStatic(data, us[i], x0); |
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} |
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crocoddyl::SolverFDDP ddp(problem); |
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ddp.setCandidate(xs, us, false); |
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boost::shared_ptr<crocoddyl::ActionDataAbstract> runningData = |
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runningModel->createData(); |
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#ifdef CROCODDYL_WITH_CODEGEN |
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// Code generation of the running an terminal models |
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typedef CppAD::AD<CppAD::cg::CG<double> > ADScalar; |
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boost::shared_ptr<crocoddyl::ActionModelAbstractTpl<ADScalar> > |
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ad_runningModel, ad_terminalModel; |
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if (robot.robot_name == "Talos_arm") { |
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crocoddyl::benchmark::build_arm_action_models(ad_runningModel, |
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ad_terminalModel); |
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} else if (robot.robot_name == "Kinova_arm") { |
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crocoddyl::benchmark::build_arm_kinova_action_models(ad_runningModel, |
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ad_terminalModel); |
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} else { |
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crocoddyl::benchmark::build_contact_action_models(robot, ad_runningModel, |
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ad_terminalModel); |
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} |
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boost::shared_ptr<crocoddyl::ActionModelAbstract> cg_runningModel = |
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boost::make_shared<crocoddyl::ActionModelCodeGen>( |
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ad_runningModel, runningModel, robot.robot_name + "_running_cg"); |
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boost::shared_ptr<crocoddyl::ActionModelAbstract> cg_terminalModel = |
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boost::make_shared<crocoddyl::ActionModelCodeGen>( |
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ad_terminalModel, terminalModel, robot.robot_name + "_terminal_cg"); |
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// Defining the shooting problem for both cases: with and without code |
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// generation |
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std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > |
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cg_runningModels(N, cg_runningModel); |
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boost::shared_ptr<crocoddyl::ShootingProblem> cg_problem = |
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boost::make_shared<crocoddyl::ShootingProblem>(x0, cg_runningModels, |
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cg_terminalModel); |
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// Check that code-generated action model is the same as original. |
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/**************************************************************************/ |
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crocoddyl::SolverFDDP cg_ddp(cg_problem); |
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cg_ddp.setCandidate(xs, us, false); |
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boost::shared_ptr<crocoddyl::ActionDataAbstract> cg_runningData = |
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cg_runningModel->createData(); |
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Eigen::VectorXd x_rand = cg_runningModel->get_state()->rand(); |
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Eigen::VectorXd u_rand = Eigen::VectorXd::Random(cg_runningModel->get_nu()); |
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runningModel->calc(runningData, x_rand, u_rand); |
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runningModel->calcDiff(runningData, x_rand, u_rand); |
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cg_runningModel->calc(cg_runningData, x_rand, u_rand); |
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cg_runningModel->calcDiff(cg_runningData, x_rand, u_rand); |
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assert_pretty(cg_runningData->xnext.isApprox(runningData->xnext), |
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"Problem in xnext"); |
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assert_pretty(std::abs(cg_runningData->cost - runningData->cost) < 1e-10, |
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"Problem in cost"); |
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assert_pretty(cg_runningData->Lx.isApprox(runningData->Lx), "Problem in Lx"); |
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assert_pretty(cg_runningData->Lu.isApprox(runningData->Lu), "Problem in Lu"); |
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assert_pretty(cg_runningData->Lxx.isApprox(runningData->Lxx), |
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"Problem in Lxx"); |
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assert_pretty(cg_runningData->Lxu.isApprox(runningData->Lxu), |
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"Problem in Lxu"); |
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assert_pretty(cg_runningData->Luu.isApprox(runningData->Luu), |
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"Problem in Luu"); |
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assert_pretty(cg_runningData->Fx.isApprox(runningData->Fx), "Problem in Fx"); |
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assert_pretty(cg_runningData->Fu.isApprox(runningData->Fu), "Problem in Fu"); |
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#endif // CROCODDYL_WITH_CODEGEN |
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/******************************* create csv file |
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* *******************************/ |
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const std::string csv_filename = "/tmp/" + robot.robot_name + "_" + |
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std::to_string(state->get_nq()) + |
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"DoF.bench"; |
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CsvStream csv(csv_filename); |
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csv << "fn_name" |
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<< "nthreads" |
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<< "with_cg" |
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<< "mean" |
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<< "stddev" |
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<< "max" |
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<< "min" |
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<< "mean_per_nodes" |
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<< "stddev_per_nodes" << csv.endl; |
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/*******************************************************************************/ |
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/*********************************** TIMINGS |
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* ***********************************/ |
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Eigen::ArrayXd duration(T); |
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Eigen::ArrayXd avg(CROCODDYL_WITH_NTHREADS); |
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Eigen::ArrayXd stddev(CROCODDYL_WITH_NTHREADS); |
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/*******************************************************************************/ |
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/****************************** ACTION MODEL TIMINGS |
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* ***************************/ |
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std::cout << "Without Code Generation:" << std::endl; |
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problem->calc(xs, us); |
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// calcDiff timings |
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for (int ithread = 0; ithread < CROCODDYL_WITH_NTHREADS; ++ithread) { |
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duration.setZero(); |
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for (unsigned int i = 0; i < T; ++i) { |
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crocoddyl::Timer timer; |
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#ifdef CROCODDYL_WITH_MULTITHREADING |
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#pragma omp parallel for num_threads(ithread + 1) |
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#endif |
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for (unsigned int j = 0; j < N; ++j) { |
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runningModels[j]->calcDiff(problem->get_runningDatas()[j], xs[j], |
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us[j]); |
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} |
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duration[i] = timer.get_us_duration(); |
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} |
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avg[ithread] = AVG(duration); |
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stddev[ithread] = STDDEV(duration); |
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std::cout << ithread + 1 << " threaded calcDiff [us]:\t" << avg[ithread] |
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<< " +- " << stddev[ithread] << " (max: " << duration.maxCoeff() |
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<< ", min: " << duration.minCoeff() |
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<< ", per nodes: " << avg[ithread] * (ithread + 1) / N << " +- " |
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<< stddev[ithread] * (ithread + 1) / N << ")" << std::endl; |
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csv << "calcDiff" << (ithread + 1) << false << avg[ithread] |
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<< stddev[ithread] << duration.maxCoeff() << duration.minCoeff() |
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<< avg[ithread] * (ithread + 1) / N |
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<< stddev[ithread] * (ithread + 1) / N << csv.endl; |
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} |
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// calc timings |
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for (int ithread = 0; ithread < CROCODDYL_WITH_NTHREADS; ++ithread) { |
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duration.setZero(); |
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for (unsigned int i = 0; i < T; ++i) { |
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crocoddyl::Timer timer; |
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#ifdef CROCODDYL_WITH_MULTITHREADING |
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#pragma omp parallel for num_threads(ithread + 1) |
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#endif |
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for (unsigned int j = 0; j < N; ++j) { |
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runningModels[j]->calc(problem->get_runningDatas()[j], xs[j], us[j]); |
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} |
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duration[i] = timer.get_us_duration(); |
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} |
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avg[ithread] = AVG(duration); |
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stddev[ithread] = STDDEV(duration); |
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std::cout << ithread + 1 << " threaded calc [us]: \t" << avg[ithread] |
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<< " +- " << stddev[ithread] << " (max: " << duration.maxCoeff() |
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<< ", min: " << duration.minCoeff() |
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<< ", per nodes: " << avg[ithread] * (ithread + 1) / N << " +- " |
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<< stddev[ithread] * (ithread + 1) / N << ")" << std::endl; |
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csv << "calc" << (ithread + 1) << false << avg[ithread] << stddev[ithread] |
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<< duration.maxCoeff() << duration.minCoeff() |
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<< avg[ithread] * (ithread + 1) / N |
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<< stddev[ithread] * (ithread + 1) / N << csv.endl; |
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} |
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/*******************************************************************************/ |
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/******************* DDP BACKWARD AND FORWARD PASSES TIMINGS |
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* *******************/ |
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// Backward pass timings |
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ddp.calcDiff(); |
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duration.setZero(); |
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for (unsigned int i = 0; i < T; ++i) { |
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crocoddyl::Timer timer; |
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ddp.backwardPass(); |
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duration[i] = timer.get_us_duration(); |
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} |
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double avg_bp = AVG(duration); |
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double stddev_bp = STDDEV(duration); |
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std::cout << "backwardPass [us]:\t\t" << avg_bp << " +- " << stddev_bp |
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<< " (max: " << duration.maxCoeff() |
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<< ", min: " << duration.minCoeff() << ", per nodes: " << avg_bp / N |
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<< " +- " << stddev_bp / N << ")" << std::endl; |
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csv << "backwardPass" << 1 << false << avg_bp << stddev_bp |
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<< duration.maxCoeff() << duration.minCoeff() << avg_bp / N |
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<< stddev_bp / N << csv.endl; |
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// Forward pass timings |
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duration.setZero(); |
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✗ |
for (unsigned int i = 0; i < T; ++i) { |
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crocoddyl::Timer timer; |
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ddp.forwardPass(0.005); |
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duration[i] = timer.get_us_duration(); |
| 268 |
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} |
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double avg_fp = AVG(duration); |
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double stddev_fp = STDDEV(duration); |
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std::cout << "forwardPass [us]: \t\t" << avg_fp << " +- " << stddev_fp |
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<< " (max: " << duration.maxCoeff() |
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<< ", min: " << duration.minCoeff() << ", per nodes: " << avg_fp / N |
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<< " +- " << stddev_fp / N << ")" << std::endl; |
| 275 |
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csv << "forwardPass" << 1 << false << avg_fp << stddev_fp |
| 277 |
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<< duration.maxCoeff() << duration.minCoeff() << avg_fp / N |
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<< stddev_fp / N << csv.endl; |
| 279 |
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#ifdef CROCODDYL_WITH_CODEGEN |
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| 282 |
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/*******************************************************************************/ |
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/*************************** CODE GENERATION TIMINGS |
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* ***************************/ |
| 285 |
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/*******************************************************************************/ |
| 286 |
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|
| 287 |
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/*******************************************************************************/ |
| 288 |
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/****************************** ACTION MODEL TIMINGS |
| 289 |
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* ***************************/ |
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std::cout << std::endl << "With Code Generation:" << std::endl; |
| 291 |
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// calcDiff timings |
| 292 |
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cg_problem->calc(xs, us); |
| 293 |
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for (int ithread = 0; ithread < CROCODDYL_WITH_NTHREADS; ++ithread) { |
| 294 |
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duration.setZero(); |
| 295 |
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for (unsigned int i = 0; i < T; ++i) { |
| 296 |
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crocoddyl::Timer timer; |
| 297 |
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#ifdef CROCODDYL_WITH_MULTITHREADING |
| 298 |
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#pragma omp parallel for num_threads(ithread + 1) |
| 299 |
|
|
#endif |
| 300 |
|
|
for (unsigned int j = 0; j < N; ++j) { |
| 301 |
|
|
cg_runningModels[j]->calcDiff(cg_problem->get_runningDatas()[j], xs[j], |
| 302 |
|
|
us[j]); |
| 303 |
|
|
} |
| 304 |
|
|
duration[i] = timer.get_us_duration(); |
| 305 |
|
|
} |
| 306 |
|
|
avg[ithread] = AVG(duration); |
| 307 |
|
|
stddev[ithread] = STDDEV(duration); |
| 308 |
|
|
std::cout << ithread + 1 << " threaded calcDiff [us]:\t" << avg[ithread] |
| 309 |
|
|
<< " +- " << stddev[ithread] << " (max: " << duration.maxCoeff() |
| 310 |
|
|
<< ", min: " << duration.minCoeff() |
| 311 |
|
|
<< ", per nodes: " << avg[ithread] * (ithread + 1) / N << " +- " |
| 312 |
|
|
<< stddev[ithread] * (ithread + 1) / N << ")" << std::endl; |
| 313 |
|
|
csv << "calcDiff" << (ithread + 1) << true << avg[ithread] |
| 314 |
|
|
<< stddev[ithread] << duration.maxCoeff() << duration.minCoeff() |
| 315 |
|
|
<< avg[ithread] * (ithread + 1) / N |
| 316 |
|
|
<< stddev[ithread] * (ithread + 1) / N << csv.endl; |
| 317 |
|
|
} |
| 318 |
|
|
|
| 319 |
|
|
// calc timings |
| 320 |
|
|
for (int ithread = 0; ithread < CROCODDYL_WITH_NTHREADS; ++ithread) { |
| 321 |
|
|
duration.setZero(); |
| 322 |
|
|
for (unsigned int i = 0; i < T; ++i) { |
| 323 |
|
|
crocoddyl::Timer timer; |
| 324 |
|
|
#ifdef CROCODDYL_WITH_MULTITHREADING |
| 325 |
|
|
#pragma omp parallel for num_threads(ithread + 1) |
| 326 |
|
|
#endif |
| 327 |
|
|
for (unsigned int j = 0; j < N; ++j) { |
| 328 |
|
|
cg_runningModels[j]->calc(cg_problem->get_runningDatas()[j], xs[j], |
| 329 |
|
|
us[j]); |
| 330 |
|
|
} |
| 331 |
|
|
duration[i] = timer.get_us_duration(); |
| 332 |
|
|
} |
| 333 |
|
|
avg[ithread] = AVG(duration); |
| 334 |
|
|
stddev[ithread] = STDDEV(duration); |
| 335 |
|
|
std::cout << ithread + 1 << " threaded calc [us]: \t" << avg[ithread] |
| 336 |
|
|
<< " +- " << stddev[ithread] << " (max: " << duration.maxCoeff() |
| 337 |
|
|
<< ", min: " << duration.minCoeff() |
| 338 |
|
|
<< ", per nodes: " << avg[ithread] * (ithread + 1) / N << " +- " |
| 339 |
|
|
<< stddev[ithread] * (ithread + 1) / N << ")" << std::endl; |
| 340 |
|
|
csv << "calc" << (ithread + 1) << true << avg[ithread] << stddev[ithread] |
| 341 |
|
|
<< duration.maxCoeff() << duration.minCoeff() |
| 342 |
|
|
<< avg[ithread] * (ithread + 1) / N |
| 343 |
|
|
<< stddev[ithread] * (ithread + 1) / N << csv.endl; |
| 344 |
|
|
} |
| 345 |
|
|
|
| 346 |
|
|
/*******************************************************************************/ |
| 347 |
|
|
/******************* DDP BACKWARD AND FORWARD PASSES TIMINGS |
| 348 |
|
|
* *******************/ |
| 349 |
|
|
// Backward pass timings |
| 350 |
|
|
cg_ddp.calcDiff(); |
| 351 |
|
|
for (unsigned int i = 0; i < T; ++i) { |
| 352 |
|
|
crocoddyl::Timer timer; |
| 353 |
|
|
cg_ddp.backwardPass(); |
| 354 |
|
|
duration[i] = timer.get_us_duration(); |
| 355 |
|
|
} |
| 356 |
|
|
avg_bp = AVG(duration); |
| 357 |
|
|
stddev_bp = STDDEV(duration); |
| 358 |
|
|
std::cout << "backwardPass [us]:\t\t" << avg_bp << " +- " << stddev_bp |
| 359 |
|
|
<< " (max: " << duration.maxCoeff() |
| 360 |
|
|
<< ", min: " << duration.minCoeff() << ", per nodes: " << avg_bp / N |
| 361 |
|
|
<< " +- " << stddev_bp / N << ")" << std::endl; |
| 362 |
|
|
csv << "backwardPass" << 1 << true << avg_bp << stddev_bp |
| 363 |
|
|
<< duration.maxCoeff() << duration.minCoeff() << avg_bp / N |
| 364 |
|
|
<< stddev_bp / N << csv.endl; |
| 365 |
|
|
|
| 366 |
|
|
// Forward pass timings |
| 367 |
|
|
for (unsigned int i = 0; i < T; ++i) { |
| 368 |
|
|
crocoddyl::Timer timer; |
| 369 |
|
|
cg_ddp.forwardPass(0.005); |
| 370 |
|
|
duration[i] = timer.get_us_duration(); |
| 371 |
|
|
} |
| 372 |
|
|
avg_fp = AVG(duration); |
| 373 |
|
|
stddev_fp = STDDEV(duration); |
| 374 |
|
|
std::cout << "forwardPass [us]: \t\t" << avg_fp << " +- " << stddev_fp |
| 375 |
|
|
<< " (max: " << duration.maxCoeff() |
| 376 |
|
|
<< ", min: " << duration.minCoeff() << ", per nodes: " << avg_fp / N |
| 377 |
|
|
<< " +- " << stddev_fp / N << ")" << std::endl; |
| 378 |
|
|
|
| 379 |
|
|
csv << "forwardPass" << 1 << true << avg_fp << stddev_fp |
| 380 |
|
|
<< duration.maxCoeff() << duration.minCoeff() << avg_fp / N |
| 381 |
|
|
<< stddev_fp / N << csv.endl; |
| 382 |
|
|
|
| 383 |
|
|
#endif // CROCODDYL_WITH_CODEGEN |
| 384 |
|
|
} |
| 385 |
|
|
|
| 386 |
|
✗ |
int main() { |
| 387 |
|
|
// Arm Manipulation Benchmarks |
| 388 |
|
✗ |
std::cout << "********************Talos 4DoF Arm******************" |
| 389 |
|
✗ |
<< std::endl; |
| 390 |
|
✗ |
std::vector<std::string> contact_names; |
| 391 |
|
✗ |
std::vector<crocoddyl::ContactType> contact_types; |
| 392 |
|
|
RobotEENames talosArm4Dof( |
| 393 |
|
|
"Talos_arm", contact_names, contact_types, |
| 394 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR "/talos_data/robots/talos_left_arm.urdf", |
| 395 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR "/talos_data/srdf/talos.srdf", |
| 396 |
|
✗ |
"gripper_left_joint", "half_sitting"); |
| 397 |
|
|
|
| 398 |
|
✗ |
print_benchmark(talosArm4Dof); |
| 399 |
|
|
// Arm Manipulation Benchmarks |
| 400 |
|
✗ |
std::cout << "******************** Kinova Arm ******************" |
| 401 |
|
✗ |
<< std::endl; |
| 402 |
|
|
RobotEENames kinovaArm( |
| 403 |
|
|
"Kinova_arm", contact_names, contact_types, |
| 404 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR "/kinova_description/robots/kinova.urdf", |
| 405 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR "/kinova_description/srdf/kinova.srdf", |
| 406 |
|
✗ |
"gripper_left_joint", "arm_up"); |
| 407 |
|
|
|
| 408 |
|
✗ |
print_benchmark(kinovaArm); |
| 409 |
|
|
|
| 410 |
|
|
// Quadruped Solo Benchmarks |
| 411 |
|
✗ |
std::cout << "********************Quadruped Solo******************" |
| 412 |
|
✗ |
<< std::endl; |
| 413 |
|
✗ |
contact_names.clear(); |
| 414 |
|
✗ |
contact_types.clear(); |
| 415 |
|
✗ |
contact_names.push_back("FR_KFE"); |
| 416 |
|
✗ |
contact_names.push_back("HL_KFE"); |
| 417 |
|
✗ |
contact_types.push_back(crocoddyl::Contact3D); |
| 418 |
|
✗ |
contact_types.push_back(crocoddyl::Contact3D); |
| 419 |
|
|
RobotEENames quadrupedSolo( |
| 420 |
|
|
"Solo", contact_names, contact_types, |
| 421 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR "/solo_description/robots/solo.urdf", |
| 422 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR "/solo_description/srdf/solo.srdf", "HL_KFE", |
| 423 |
|
✗ |
"standing"); |
| 424 |
|
|
|
| 425 |
|
✗ |
print_benchmark(quadrupedSolo); |
| 426 |
|
|
|
| 427 |
|
|
// Quadruped Anymal Benchmarks |
| 428 |
|
✗ |
std::cout << "********************Quadruped Anymal******************" |
| 429 |
|
✗ |
<< std::endl; |
| 430 |
|
✗ |
contact_names.clear(); |
| 431 |
|
✗ |
contact_types.clear(); |
| 432 |
|
✗ |
contact_names.push_back("RF_KFE"); |
| 433 |
|
✗ |
contact_names.push_back("LF_KFE"); |
| 434 |
|
✗ |
contact_names.push_back("LH_KFE"); |
| 435 |
|
✗ |
contact_types.push_back(crocoddyl::Contact3D); |
| 436 |
|
✗ |
contact_types.push_back(crocoddyl::Contact3D); |
| 437 |
|
✗ |
contact_types.push_back(crocoddyl::Contact3D); |
| 438 |
|
|
RobotEENames quadrupedAnymal( |
| 439 |
|
|
"Anymal", contact_names, contact_types, |
| 440 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR |
| 441 |
|
|
"/anymal_b_simple_description/robots/anymal.urdf", |
| 442 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR |
| 443 |
|
|
"/anymal_b_simple_description/srdf/anymal.srdf", |
| 444 |
|
✗ |
"RH_KFE", "standing"); |
| 445 |
|
|
|
| 446 |
|
✗ |
print_benchmark(quadrupedAnymal); |
| 447 |
|
|
|
| 448 |
|
|
// Quadruped HyQ Benchmarks |
| 449 |
|
✗ |
std::cout << "******************** Quadruped HyQ ******************" |
| 450 |
|
✗ |
<< std::endl; |
| 451 |
|
✗ |
contact_names.clear(); |
| 452 |
|
✗ |
contact_types.clear(); |
| 453 |
|
✗ |
contact_names.push_back("rf_kfe_joint"); |
| 454 |
|
✗ |
contact_names.push_back("lf_kfe_joint"); |
| 455 |
|
✗ |
contact_names.push_back("lh_kfe_joint"); |
| 456 |
|
✗ |
contact_types.push_back(crocoddyl::Contact3D); |
| 457 |
|
✗ |
contact_types.push_back(crocoddyl::Contact3D); |
| 458 |
|
✗ |
contact_types.push_back(crocoddyl::Contact3D); |
| 459 |
|
|
RobotEENames quadrupedHyQ("HyQ", contact_names, contact_types, |
| 460 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR |
| 461 |
|
|
"/hyq_description/robots/hyq_no_sensors.urdf", |
| 462 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR |
| 463 |
|
|
"/hyq_description/srdf/hyq.srdf", |
| 464 |
|
✗ |
"rh_kfe_joint", "standing"); |
| 465 |
|
|
|
| 466 |
|
✗ |
print_benchmark(quadrupedHyQ); |
| 467 |
|
|
|
| 468 |
|
|
// Biped icub Benchmarks |
| 469 |
|
✗ |
std::cout << "********************Biped iCub ***********************" |
| 470 |
|
✗ |
<< std::endl; |
| 471 |
|
✗ |
contact_names.clear(); |
| 472 |
|
✗ |
contact_types.clear(); |
| 473 |
|
✗ |
contact_names.push_back("r_ankle_roll"); |
| 474 |
|
✗ |
contact_names.push_back("l_ankle_roll"); |
| 475 |
|
✗ |
contact_types.push_back(crocoddyl::Contact6D); |
| 476 |
|
✗ |
contact_types.push_back(crocoddyl::Contact6D); |
| 477 |
|
|
|
| 478 |
|
|
RobotEENames bipedIcub( |
| 479 |
|
|
"iCub", contact_names, contact_types, |
| 480 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR "/icub_description/robots/icub_reduced.urdf", |
| 481 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR "/icub_description/srdf/icub.srdf", |
| 482 |
|
✗ |
"r_wrist_yaw", "half_sitting"); |
| 483 |
|
✗ |
print_benchmark(bipedIcub); |
| 484 |
|
|
|
| 485 |
|
|
// Biped icub Benchmarks |
| 486 |
|
✗ |
std::cout << "********************Biped Talos***********************" |
| 487 |
|
✗ |
<< std::endl; |
| 488 |
|
✗ |
contact_names.clear(); |
| 489 |
|
✗ |
contact_types.clear(); |
| 490 |
|
✗ |
contact_names.push_back("leg_right_6_joint"); |
| 491 |
|
✗ |
contact_names.push_back("leg_left_6_joint"); |
| 492 |
|
✗ |
contact_types.push_back(crocoddyl::Contact6D); |
| 493 |
|
✗ |
contact_types.push_back(crocoddyl::Contact6D); |
| 494 |
|
|
|
| 495 |
|
|
RobotEENames bipedTalos( |
| 496 |
|
|
"Talos", contact_names, contact_types, |
| 497 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR "/talos_data/robots/talos_reduced.urdf", |
| 498 |
|
|
EXAMPLE_ROBOT_DATA_MODEL_DIR "/talos_data/srdf/talos.srdf", |
| 499 |
|
✗ |
"arm_right_7_joint", "half_sitting"); |
| 500 |
|
✗ |
print_benchmark(bipedTalos); |
| 501 |
|
|
|
| 502 |
|
✗ |
return 0; |
| 503 |
|
|
} |
| 504 |
|
|
|