crocoddyl  1.8.1
Contact RObot COntrol by Differential DYnamic programming Library (Crocoddyl)
box-ddp.cpp
1 // BSD 3-Clause License
3 //
4 // Copyright (C) 2019-2021, CNRS-LAAS, University of Edinburgh
5 // Copyright note valid unless otherwise stated in individual files.
6 // All rights reserved.
8 
9 #include <iostream>
10 
11 #include "crocoddyl/core/solvers/box-ddp.hpp"
12 #include "crocoddyl/core/utils/exception.hpp"
13 
14 namespace crocoddyl {
15 
16 SolverBoxDDP::SolverBoxDDP(boost::shared_ptr<ShootingProblem> problem)
17  : SolverDDP(problem), qp_(problem->get_runningModels()[0]->get_nu(), 100, 0.1, 1e-5, 0.) {
18  allocateData();
19 
20  const std::size_t n_alphas = 10;
21  alphas_.resize(n_alphas);
22  for (std::size_t n = 0; n < n_alphas; ++n) {
23  alphas_[n] = 1. / pow(2., static_cast<double>(n));
24  }
25  // Change the default convergence tolerance since the gradient of the Lagrangian is smaller
26  // than an unconstrained OC problem (i.e. gradient = Qu - mu^T * C where mu > 0 and C defines
27  // the inequality matrix that bounds the control); and we don't have access to mu from the
28  // box QP.
29  th_stop_ = 5e-5;
30 }
31 
32 SolverBoxDDP::~SolverBoxDDP() {}
33 
36 
37  const std::size_t T = problem_->get_T();
38  Quu_inv_.resize(T);
39  const std::size_t nu = problem_->get_nu_max();
40  for (std::size_t t = 0; t < T; ++t) {
41  Quu_inv_[t] = Eigen::MatrixXd::Zero(nu, nu);
42  }
43  du_lb_.resize(nu);
44  du_ub_.resize(nu);
45 }
46 
47 void SolverBoxDDP::computeGains(const std::size_t t) {
48  const std::size_t nu = problem_->get_runningModels()[t]->get_nu();
49  if (nu > 0) {
50  if (!problem_->get_runningModels()[t]->get_has_control_limits() || !is_feasible_) {
51  // No control limits on this model: Use vanilla DDP
53  return;
54  }
55 
56  du_lb_.head(nu) = problem_->get_runningModels()[t]->get_u_lb() - us_[t].head(nu);
57  du_ub_.head(nu) = problem_->get_runningModels()[t]->get_u_ub() - us_[t].head(nu);
58 
59  const BoxQPSolution& boxqp_sol =
60  qp_.solve(Quu_[t].topLeftCorner(nu, nu), Qu_[t].head(nu), du_lb_.head(nu), du_ub_.head(nu), k_[t].head(nu));
61 
62  // Compute controls
63  Quu_inv_[t].topLeftCorner(nu, nu).setZero();
64  for (std::size_t i = 0; i < boxqp_sol.free_idx.size(); ++i) {
65  for (std::size_t j = 0; j < boxqp_sol.free_idx.size(); ++j) {
66  Quu_inv_[t](boxqp_sol.free_idx[i], boxqp_sol.free_idx[j]) = boxqp_sol.Hff_inv(i, j);
67  }
68  }
69  K_[t].topRows(nu).noalias() = Quu_inv_[t].topLeftCorner(nu, nu) * Qxu_[t].leftCols(nu).transpose();
70  k_[t].topRows(nu) = -boxqp_sol.x;
71 
72  // The box-QP clamped the gradient direction; this is important for accounting
73  // the algorithm advancement (i.e. stopping criteria)
74  for (std::size_t i = 0; i < boxqp_sol.clamped_idx.size(); ++i) {
75  Qu_[t].head(nu)(boxqp_sol.clamped_idx[i]) = 0.;
76  }
77  }
78 }
79 
80 void SolverBoxDDP::forwardPass(double steplength) {
81  if (steplength > 1. || steplength < 0.) {
82  throw_pretty("Invalid argument: "
83  << "invalid step length, value is between 0. to 1.");
84  }
85  cost_try_ = 0.;
86  xnext_ = problem_->get_x0();
87  const std::size_t T = problem_->get_T();
88  const std::vector<boost::shared_ptr<ActionModelAbstract> >& models = problem_->get_runningModels();
89  const std::vector<boost::shared_ptr<ActionDataAbstract> >& datas = problem_->get_runningDatas();
90  for (std::size_t t = 0; t < T; ++t) {
91  const boost::shared_ptr<ActionModelAbstract>& m = models[t];
92  const boost::shared_ptr<ActionDataAbstract>& d = datas[t];
93  const std::size_t nu = m->get_nu();
94 
95  xs_try_[t] = xnext_;
96  m->get_state()->diff(xs_[t], xs_try_[t], dx_[t]);
97  if (nu != 0) {
98  us_try_[t].head(nu).noalias() = us_[t].head(nu) - k_[t].head(nu) * steplength - K_[t].topRows(nu) * dx_[t];
99  if (m->get_has_control_limits()) { // clamp control
100  us_try_[t].head(nu) = us_try_[t].head(nu).cwiseMax(m->get_u_lb()).cwiseMin(m->get_u_ub());
101  }
102  m->calc(d, xs_try_[t], us_try_[t].head(nu));
103  } else {
104  m->calc(d, xs_try_[t]);
105  }
106  xnext_ = d->xnext;
107  cost_try_ += d->cost;
108 
109  if (raiseIfNaN(cost_try_)) {
110  throw_pretty("forward_error");
111  }
112  if (raiseIfNaN(xnext_.lpNorm<Eigen::Infinity>())) {
113  throw_pretty("forward_error");
114  }
115  }
116 
117  const boost::shared_ptr<ActionModelAbstract>& m = problem_->get_terminalModel();
118  const boost::shared_ptr<ActionDataAbstract>& d = problem_->get_terminalData();
119  if ((is_feasible_) || (steplength == 1)) {
120  xs_try_.back() = xnext_;
121  } else {
122  m->get_state()->integrate(xnext_, fs_.back() * (steplength - 1), xs_try_.back());
123  }
124  m->calc(d, xs_try_.back());
125  cost_try_ += d->cost;
126 
127  if (raiseIfNaN(cost_try_)) {
128  throw_pretty("forward_error");
129  }
130 }
131 
132 const std::vector<Eigen::MatrixXd>& SolverBoxDDP::get_Quu_inv() const { return Quu_inv_; }
133 
134 } // namespace crocoddyl
crocoddyl::SolverDDP::computeGains
virtual void computeGains(const std::size_t t)
Compute the feedforward and feedback terms using a Cholesky decomposition.
Definition: ddp.cpp:339
crocoddyl::SolverBoxDDP::computeGains
virtual void computeGains(const std::size_t t)
Compute the feedforward and feedback terms using a Cholesky decomposition.
Definition: box-ddp.cpp:47
crocoddyl::BoxQP::solve
const BoxQPSolution & solve(const Eigen::MatrixXd &H, const Eigen::VectorXd &q, const Eigen::VectorXd &lb, const Eigen::VectorXd &ub, const Eigen::VectorXd &xinit)
Compute the solution of bound-constrained QP based on Newton projection.
Definition: box-qp.cpp:58
crocoddyl::BoxQPSolution::free_idx
std::vector< size_t > free_idx
Free space indexes.
Definition: box-qp.hpp:50
crocoddyl::BoxQPSolution::Hff_inv
Eigen::MatrixXd Hff_inv
Inverse of the free space Hessian.
Definition: box-qp.hpp:48
crocoddyl::SolverAbstract::is_feasible_
bool is_feasible_
Label that indicates is the iteration is feasible.
Definition: solver-base.hpp:264
crocoddyl::SolverBoxDDP::allocateData
virtual void allocateData()
Allocate all the internal data needed for the solver.
Definition: box-ddp.cpp:34
crocoddyl::SolverDDP::Qu_
std::vector< Eigen::VectorXd > Qu_
Gradient of the Hamiltonian.
Definition: ddp.hpp:329
crocoddyl::SolverDDP::Qxu_
std::vector< Eigen::MatrixXd > Qxu_
Hessian of the Hamiltonian.
Definition: ddp.hpp:326
crocoddyl::SolverBoxDDP::forwardPass
virtual void forwardPass(const double steplength)
Run the forward pass or rollout.
Definition: box-ddp.cpp:80
crocoddyl::SolverDDP::Quu_
std::vector< Eigen::MatrixXd > Quu_
Hessian of the Hamiltonian.
Definition: ddp.hpp:327
crocoddyl::SolverDDP::cost_try_
double cost_try_
Total cost computed by line-search procedure.
Definition: ddp.hpp:316
crocoddyl::SolverAbstract::th_stop_
double th_stop_
Tolerance for stopping the algorithm.
Definition: solver-base.hpp:274
crocoddyl::SolverDDP::fs_
std::vector< Eigen::VectorXd > fs_
Gaps/defects between shooting nodes.
Definition: ddp.hpp:332
crocoddyl::SolverDDP::us_try_
std::vector< Eigen::VectorXd > us_try_
Control trajectory computed by line-search procedure.
Definition: ddp.hpp:318
crocoddyl::SolverAbstract::problem_
boost::shared_ptr< ShootingProblem > problem_
optimal control problem
Definition: solver-base.hpp:260
crocoddyl::SolverDDP::K_
std::vector< MatrixXdRowMajor > K_
Feedback gains.
Definition: ddp.hpp:330
crocoddyl::SolverAbstract::us_
std::vector< Eigen::VectorXd > us_
Control trajectory.
Definition: solver-base.hpp:262
crocoddyl::SolverAbstract::xs_
std::vector< Eigen::VectorXd > xs_
State trajectory.
Definition: solver-base.hpp:261
crocoddyl::SolverDDP::xs_try_
std::vector< Eigen::VectorXd > xs_try_
State trajectory computed by line-search procedure.
Definition: ddp.hpp:317
crocoddyl::BoxQPSolution
Box QP solution.
Definition: box-qp.hpp:28
crocoddyl::SolverDDP::allocateData
virtual void allocateData()
Allocate all the internal data needed for the solver.
Definition: ddp.cpp:380
crocoddyl::BoxQPSolution::clamped_idx
std::vector< size_t > clamped_idx
Clamped space indexes.
Definition: box-qp.hpp:51
crocoddyl::SolverDDP::xnext_
Eigen::VectorXd xnext_
Next state.
Definition: ddp.hpp:334
crocoddyl::BoxQPSolution::x
Eigen::VectorXd x
Decision vector.
Definition: box-qp.hpp:49
crocoddyl::SolverDDP::k_
std::vector< Eigen::VectorXd > k_
Feed-forward terms.
Definition: ddp.hpp:331