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/////////////////////////////////////////////////////////////////////////////// |
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// BSD 3-Clause License |
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// |
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// Copyright (C) 2019-2025, LAAS-CNRS, University of Edinburgh, |
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// 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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#include "crocoddyl/core/solvers/fddp.hpp" |
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#include "python/crocoddyl/core/core.hpp" |
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#include "python/crocoddyl/utils/copyable.hpp" |
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#define SCALAR_float64 |
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namespace crocoddyl { |
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namespace python { |
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BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolverFDDP_solves, SolverFDDP::solve, 0, |
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5) |
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BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolverFDDP_computeDirections, |
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SolverDDP::computeDirection, 0, 1) |
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void exposeSolverFDDP() { |
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#ifdef SCALAR_float64 |
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bp::register_ptr_to_python<std::shared_ptr<SolverFDDP> >(); |
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bp::class_<SolverFDDP, bp::bases<SolverDDP> >( |
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"SolverFDDP", |
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"Feasibility-driven DDP (FDDP) solver.\n\n" |
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"The FDDP solver computes an optimal trajectory and control commands by " |
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"iterates\n" |
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"running backward and forward passes. The backward-pass updates locally " |
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"the\n" |
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"quadratic approximation of the problem and computes descent direction,\n" |
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"and the forward-pass rollouts this new policy by integrating the system " |
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"dynamics\n" |
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"along a tuple of optimized control commands U*.\n" |
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":param shootingProblem: shooting problem (list of action models along " |
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"trajectory.)", |
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bp::init<std::shared_ptr<ShootingProblem> >( |
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bp::args("self", "problem"), |
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"Initialize the vector dimension.\n\n" |
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":param problem: shooting problem.")) |
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.def("solve", &SolverFDDP::solve, |
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SolverFDDP_solves( |
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bp::args("self", "init_xs", "init_us", "maxiter", "is_feasible", |
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"init_reg"), |
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"Compute the optimal trajectory xopt, uopt as lists of T+1 and " |
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"T terms.\n\n" |
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"From an initial guess init_xs,init_us (feasible or not), " |
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"iterate\n" |
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"over computeDirection and tryStep until stoppingCriteria is " |
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"below\n" |
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"threshold. It also describes the globalization strategy used\n" |
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"during the numerical optimization.\n" |
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":param init_xs: initial guess for state trajectory with T+1 " |
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"elements (default [])\n" |
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":param init_us: initial guess for control trajectory with T " |
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"elements (default []).\n" |
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":param maxiter: maximum allowed number of iterations (default " |
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"100).\n" |
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":param is_feasible: true if the init_xs are obtained from " |
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"integrating the init_us (rollout)\n" |
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"(default False).\n" |
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":param init_reg: initial guess for the regularization value. " |
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"Very low values are typical\n" |
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" used with very good guess points (default " |
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"1e-9).\n" |
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":returns the optimal trajectory xopt, uopt and a boolean that " |
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"describes if convergence was reached.")) |
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.def("updateExpectedImprovement", &SolverFDDP::updateExpectedImprovement, |
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bp::return_value_policy<bp::reference_existing_object>(), |
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bp::args("self"), "Update the expected improvement model\n\n") |
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.add_property("th_acceptNegStep", |
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bp::make_function(&SolverFDDP::get_th_acceptnegstep), |
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bp::make_function(&SolverFDDP::set_th_acceptnegstep), |
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"threshold for step acceptance in ascent direction") |
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.def(CopyableVisitor<SolverFDDP>()); |
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#endif |
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} |
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} // namespace python |
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} // namespace crocoddyl |
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