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/////////////////////////////////////////////////////////////////////////////// |
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// BSD 3-Clause License |
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// |
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// Copyright (C) 2019-2021, CNRS-LAAS, 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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#include "crocoddyl/core/solvers/box-ddp.hpp" |
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namespace crocoddyl { |
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SolverBoxDDP::SolverBoxDDP(std::shared_ptr<ShootingProblem> problem) |
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: SolverDDP(problem), |
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qp_(problem->get_runningModels()[0]->get_nu(), 100, 0.1, 1e-5, 0.) { |
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allocateData(); |
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const std::size_t n_alphas = 10; |
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alphas_.resize(n_alphas); |
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for (std::size_t n = 0; n < n_alphas; ++n) { |
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alphas_[n] = 1. / pow(2., static_cast<double>(n)); |
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} |
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// Change the default convergence tolerance since the gradient of the |
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// Lagrangian is smaller than an unconstrained OC problem (i.e. gradient = Qu |
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// - mu^T * C where mu > 0 and C defines the inequality matrix that bounds the |
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// control); and we don't have access to mu from the box QP. |
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th_stop_ = 5e-5; |
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} |
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SolverBoxDDP::~SolverBoxDDP() {} |
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void SolverBoxDDP::resizeData() { |
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START_PROFILER("SolverBoxDDP::resizeData"); |
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SolverDDP::resizeData(); |
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const std::size_t T = problem_->get_T(); |
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const std::vector<std::shared_ptr<ActionModelAbstract> >& models = |
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problem_->get_runningModels(); |
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for (std::size_t t = 0; t < T; ++t) { |
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const std::shared_ptr<ActionModelAbstract>& model = models[t]; |
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const std::size_t nu = model->get_nu(); |
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Quu_inv_[t].conservativeResize(nu, nu); |
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du_lb_[t].conservativeResize(nu); |
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du_ub_[t].conservativeResize(nu); |
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} |
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STOP_PROFILER("SolverBoxDDP::resizeData"); |
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} |
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void SolverBoxDDP::allocateData() { |
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SolverDDP::allocateData(); |
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const std::size_t T = problem_->get_T(); |
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Quu_inv_.resize(T); |
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du_lb_.resize(T); |
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du_ub_.resize(T); |
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const std::vector<std::shared_ptr<ActionModelAbstract> >& models = |
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problem_->get_runningModels(); |
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for (std::size_t t = 0; t < T; ++t) { |
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const std::shared_ptr<ActionModelAbstract>& model = models[t]; |
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const std::size_t nu = model->get_nu(); |
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Quu_inv_[t] = Eigen::MatrixXd::Zero(nu, nu); |
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du_lb_[t] = Eigen::VectorXd::Zero(nu); |
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du_ub_[t] = Eigen::VectorXd::Zero(nu); |
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} |
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} |
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void SolverBoxDDP::computeGains(const std::size_t t) { |
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START_PROFILER("SolverBoxDDP::computeGains"); |
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const std::size_t nu = problem_->get_runningModels()[t]->get_nu(); |
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if (nu > 0) { |
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if (!problem_->get_runningModels()[t]->get_has_control_limits() || |
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!is_feasible_) { |
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// No control limits on this model: Use vanilla DDP |
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SolverDDP::computeGains(t); |
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return; |
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} |
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du_lb_[t] = problem_->get_runningModels()[t]->get_u_lb() - us_[t]; |
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du_ub_[t] = problem_->get_runningModels()[t]->get_u_ub() - us_[t]; |
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START_PROFILER("SolverBoxDDP::boxQP"); |
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const BoxQPSolution& boxqp_sol = |
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qp_.solve(Quu_[t], Qu_[t], du_lb_[t], du_ub_[t], k_[t]); |
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START_PROFILER("SolverBoxDDP::boxQP"); |
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// Compute controls |
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START_PROFILER("SolverBoxDDP::Quu_invproj"); |
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Quu_inv_[t].setZero(); |
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for (std::size_t i = 0; i < boxqp_sol.free_idx.size(); ++i) { |
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for (std::size_t j = 0; j < boxqp_sol.free_idx.size(); ++j) { |
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Quu_inv_[t](boxqp_sol.free_idx[i], boxqp_sol.free_idx[j]) = |
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boxqp_sol.Hff_inv(i, j); |
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} |
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} |
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STOP_PROFILER("SolverBoxDDP::Quu_invproj"); |
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START_PROFILER("SolverBoxDDP::Quu_invproj_Qxu"); |
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K_[t].noalias() = Quu_inv_[t] * Qxu_[t].transpose(); |
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STOP_PROFILER("SolverBoxDDP::Quu_invproj_Qxu"); |
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k_[t] = -boxqp_sol.x; |
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// The box-QP clamped the gradient direction; this is important for |
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// accounting the algorithm advancement (i.e. stopping criteria) |
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START_PROFILER("SolverBoxDDP::Qu_proj"); |
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for (std::size_t i = 0; i < boxqp_sol.clamped_idx.size(); ++i) { |
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Qu_[t](boxqp_sol.clamped_idx[i]) = 0.; |
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} |
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STOP_PROFILER("SolverBoxDDP::Qu_proj"); |
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} |
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STOP_PROFILER("SolverBoxDDP::computeGains"); |
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} |
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void SolverBoxDDP::forwardPass(double steplength) { |
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if (steplength > 1. || steplength < 0.) { |
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throw_pretty("Invalid argument: " |
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<< "invalid step length, value is between 0. to 1."); |
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} |
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START_PROFILER("SolverBoxDDP::forwardPass"); |
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cost_try_ = 0.; |
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xnext_ = problem_->get_x0(); |
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const std::size_t T = problem_->get_T(); |
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const std::vector<std::shared_ptr<ActionModelAbstract> >& models = |
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problem_->get_runningModels(); |
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const std::vector<std::shared_ptr<ActionDataAbstract> >& datas = |
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problem_->get_runningDatas(); |
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for (std::size_t t = 0; t < T; ++t) { |
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const std::shared_ptr<ActionModelAbstract>& m = models[t]; |
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const std::shared_ptr<ActionDataAbstract>& d = datas[t]; |
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const std::size_t nu = m->get_nu(); |
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xs_try_[t] = xnext_; |
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m->get_state()->diff(xs_[t], xs_try_[t], dx_[t]); |
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if (nu != 0) { |
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us_try_[t].noalias() = us_[t] - k_[t] * steplength - K_[t] * dx_[t]; |
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if (m->get_has_control_limits()) { // clamp control |
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us_try_[t] = us_try_[t].cwiseMax(m->get_u_lb()).cwiseMin(m->get_u_ub()); |
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} |
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m->calc(d, xs_try_[t], us_try_[t]); |
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} else { |
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m->calc(d, xs_try_[t]); |
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} |
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xnext_ = d->xnext; |
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cost_try_ += d->cost; |
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if (raiseIfNaN(cost_try_)) { |
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STOP_PROFILER("SolverBoxDDP::forwardPass"); |
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throw_pretty("forward_error"); |
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} |
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if (raiseIfNaN(xnext_.lpNorm<Eigen::Infinity>())) { |
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STOP_PROFILER("SolverBoxDDP::forwardPass"); |
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throw_pretty("forward_error"); |
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} |
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} |
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const std::shared_ptr<ActionModelAbstract>& m = problem_->get_terminalModel(); |
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const std::shared_ptr<ActionDataAbstract>& d = problem_->get_terminalData(); |
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if ((is_feasible_) || (steplength == 1)) { |
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xs_try_.back() = xnext_; |
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} else { |
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m->get_state()->integrate(xnext_, fs_.back() * (steplength - 1), |
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xs_try_.back()); |
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} |
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m->calc(d, xs_try_.back()); |
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cost_try_ += d->cost; |
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if (raiseIfNaN(cost_try_)) { |
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STOP_PROFILER("SolverBoxDDP::forwardPass"); |
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throw_pretty("forward_error"); |
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} |
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} |
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const std::vector<Eigen::MatrixXd>& SolverBoxDDP::get_Quu_inv() const { |
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return Quu_inv_; |
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} |
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} // namespace crocoddyl |
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