| Line |
Branch |
Exec |
Source |
| 1 |
|
|
/////////////////////////////////////////////////////////////////////////////// |
| 2 |
|
|
// BSD 3-Clause License |
| 3 |
|
|
// |
| 4 |
|
|
// Copyright (C) 2019-2025, LAAS-CNRS, University of Edinburgh, |
| 5 |
|
|
// New York University, Max Planck Gesellschaft, |
| 6 |
|
|
// University of Oxford, Heriot-Watt University |
| 7 |
|
|
// Copyright note valid unless otherwise stated in individual files. |
| 8 |
|
|
// All rights reserved. |
| 9 |
|
|
/////////////////////////////////////////////////////////////////////////////// |
| 10 |
|
|
|
| 11 |
|
|
#include "crocoddyl/core/numdiff/action.hpp" |
| 12 |
|
|
|
| 13 |
|
|
namespace crocoddyl { |
| 14 |
|
|
|
| 15 |
|
|
template <typename Scalar> |
| 16 |
|
✗ |
ActionModelNumDiffTpl<Scalar>::ActionModelNumDiffTpl( |
| 17 |
|
|
std::shared_ptr<Base> model, bool with_gauss_approx) |
| 18 |
|
|
: Base(model->get_state(), model->get_nu(), model->get_nr(), |
| 19 |
|
✗ |
model->get_ng(), model->get_nh(), model->get_ng_T(), |
| 20 |
|
✗ |
model->get_nh_T()), |
| 21 |
|
✗ |
model_(model), |
| 22 |
|
✗ |
e_jac_(sqrt(Scalar(2.0) * std::numeric_limits<Scalar>::epsilon())), |
| 23 |
|
✗ |
with_gauss_approx_(with_gauss_approx) { |
| 24 |
|
✗ |
e_hess_ = sqrt(Scalar(2.0) * e_jac_); |
| 25 |
|
✗ |
this->set_u_lb(model_->get_u_lb()); |
| 26 |
|
✗ |
this->set_u_ub(model_->get_u_ub()); |
| 27 |
|
|
} |
| 28 |
|
|
|
| 29 |
|
|
template <typename Scalar> |
| 30 |
|
✗ |
void ActionModelNumDiffTpl<Scalar>::calc( |
| 31 |
|
|
const std::shared_ptr<ActionDataAbstract>& data, |
| 32 |
|
|
const Eigen::Ref<const VectorXs>& x, const Eigen::Ref<const VectorXs>& u) { |
| 33 |
|
✗ |
if (static_cast<std::size_t>(x.size()) != state_->get_nx()) { |
| 34 |
|
✗ |
throw_pretty( |
| 35 |
|
|
"Invalid argument: " << "x has wrong dimension (it should be " + |
| 36 |
|
|
std::to_string(state_->get_nx()) + ")"); |
| 37 |
|
|
} |
| 38 |
|
✗ |
if (static_cast<std::size_t>(u.size()) != nu_) { |
| 39 |
|
✗ |
throw_pretty( |
| 40 |
|
|
"Invalid argument: " << "u has wrong dimension (it should be " + |
| 41 |
|
|
std::to_string(nu_) + ")"); |
| 42 |
|
|
} |
| 43 |
|
✗ |
Data* d = static_cast<Data*>(data.get()); |
| 44 |
|
✗ |
model_->calc(d->data_0, x, u); |
| 45 |
|
✗ |
data->xnext = d->data_0->xnext; |
| 46 |
|
✗ |
data->cost = d->data_0->cost; |
| 47 |
|
✗ |
d->g = d->data_0->g; |
| 48 |
|
✗ |
d->h = d->data_0->h; |
| 49 |
|
|
} |
| 50 |
|
|
|
| 51 |
|
|
template <typename Scalar> |
| 52 |
|
✗ |
void ActionModelNumDiffTpl<Scalar>::calc( |
| 53 |
|
|
const std::shared_ptr<ActionDataAbstract>& data, |
| 54 |
|
|
const Eigen::Ref<const VectorXs>& x) { |
| 55 |
|
✗ |
if (static_cast<std::size_t>(x.size()) != state_->get_nx()) { |
| 56 |
|
✗ |
throw_pretty( |
| 57 |
|
|
"Invalid argument: " << "x has wrong dimension (it should be " + |
| 58 |
|
|
std::to_string(state_->get_nx()) + ")"); |
| 59 |
|
|
} |
| 60 |
|
✗ |
Data* d = static_cast<Data*>(data.get()); |
| 61 |
|
✗ |
model_->calc(d->data_0, x); |
| 62 |
|
✗ |
data->xnext = d->data_0->xnext; |
| 63 |
|
✗ |
data->cost = d->data_0->cost; |
| 64 |
|
✗ |
d->g = d->data_0->g; |
| 65 |
|
✗ |
d->h = d->data_0->h; |
| 66 |
|
|
} |
| 67 |
|
|
|
| 68 |
|
|
template <typename Scalar> |
| 69 |
|
✗ |
void ActionModelNumDiffTpl<Scalar>::calcDiff( |
| 70 |
|
|
const std::shared_ptr<ActionDataAbstract>& data, |
| 71 |
|
|
const Eigen::Ref<const VectorXs>& x, const Eigen::Ref<const VectorXs>& u) { |
| 72 |
|
✗ |
if (static_cast<std::size_t>(x.size()) != state_->get_nx()) { |
| 73 |
|
✗ |
throw_pretty( |
| 74 |
|
|
"Invalid argument: " << "x has wrong dimension (it should be " + |
| 75 |
|
|
std::to_string(state_->get_nx()) + ")"); |
| 76 |
|
|
} |
| 77 |
|
✗ |
if (static_cast<std::size_t>(u.size()) != nu_) { |
| 78 |
|
✗ |
throw_pretty( |
| 79 |
|
|
"Invalid argument: " << "u has wrong dimension (it should be " + |
| 80 |
|
|
std::to_string(nu_) + ")"); |
| 81 |
|
|
} |
| 82 |
|
✗ |
Data* d = static_cast<Data*>(data.get()); |
| 83 |
|
|
|
| 84 |
|
✗ |
const VectorXs& x0 = d->data_0->xnext; |
| 85 |
|
✗ |
const Scalar c0 = d->data_0->cost; |
| 86 |
|
✗ |
data->xnext = d->data_0->xnext; |
| 87 |
|
✗ |
data->cost = d->data_0->cost; |
| 88 |
|
✗ |
const VectorXs& g0 = d->g; |
| 89 |
|
✗ |
const VectorXs& h0 = d->h; |
| 90 |
|
✗ |
const std::size_t ndx = model_->get_state()->get_ndx(); |
| 91 |
|
✗ |
const std::size_t nu = model_->get_nu(); |
| 92 |
|
✗ |
const std::size_t ng = model_->get_ng(); |
| 93 |
|
✗ |
const std::size_t nh = model_->get_nh(); |
| 94 |
|
✗ |
d->Gx.conservativeResize(ng, ndx); |
| 95 |
|
✗ |
d->Gu.conservativeResize(ng, nu); |
| 96 |
|
✗ |
d->Hx.conservativeResize(nh, ndx); |
| 97 |
|
✗ |
d->Hu.conservativeResize(nh, nu); |
| 98 |
|
✗ |
d->du.setZero(); |
| 99 |
|
|
|
| 100 |
|
✗ |
assertStableStateFD(x); |
| 101 |
|
|
|
| 102 |
|
|
// Computing the d action(x,u) / dx |
| 103 |
|
✗ |
model_->get_state()->diff(model_->get_state()->zero(), x, d->dx); |
| 104 |
|
✗ |
d->x_norm = d->dx.norm(); |
| 105 |
|
✗ |
d->dx.setZero(); |
| 106 |
|
✗ |
d->xh_jac = e_jac_ * std::max(Scalar(1.), d->x_norm); |
| 107 |
|
✗ |
for (std::size_t ix = 0; ix < state_->get_ndx(); ++ix) { |
| 108 |
|
✗ |
d->dx(ix) = d->xh_jac; |
| 109 |
|
✗ |
model_->get_state()->integrate(x, d->dx, d->xp); |
| 110 |
|
✗ |
model_->calc(d->data_x[ix], d->xp, u); |
| 111 |
|
|
// dynamics |
| 112 |
|
✗ |
model_->get_state()->diff(x0, d->data_x[ix]->xnext, d->Fx.col(ix)); |
| 113 |
|
|
// cost |
| 114 |
|
✗ |
data->Lx(ix) = (d->data_x[ix]->cost - c0) / d->xh_jac; |
| 115 |
|
✗ |
if (get_with_gauss_approx()) { |
| 116 |
|
✗ |
d->Rx.col(ix) = (d->data_x[ix]->r - d->data_0->r) / d->xh_jac; |
| 117 |
|
|
} |
| 118 |
|
|
// constraint |
| 119 |
|
✗ |
data->Gx.col(ix) = (d->data_x[ix]->g - g0) / d->xh_jac; |
| 120 |
|
✗ |
data->Hx.col(ix) = (d->data_x[ix]->h - h0) / d->xh_jac; |
| 121 |
|
✗ |
d->dx(ix) = Scalar(0.); |
| 122 |
|
|
} |
| 123 |
|
✗ |
data->Fx /= d->xh_jac; |
| 124 |
|
|
|
| 125 |
|
|
// Computing the d action(x,u) / du |
| 126 |
|
✗ |
d->uh_jac = e_jac_ * std::max(Scalar(1.), u.norm()); |
| 127 |
|
✗ |
for (unsigned iu = 0; iu < model_->get_nu(); ++iu) { |
| 128 |
|
✗ |
d->du(iu) = d->uh_jac; |
| 129 |
|
✗ |
model_->calc(d->data_u[iu], x, u + d->du); |
| 130 |
|
|
// dynamics |
| 131 |
|
✗ |
model_->get_state()->diff(x0, d->data_u[iu]->xnext, d->Fu.col(iu)); |
| 132 |
|
|
// cost |
| 133 |
|
✗ |
data->Lu(iu) = (d->data_u[iu]->cost - c0) / d->uh_jac; |
| 134 |
|
✗ |
if (get_with_gauss_approx()) { |
| 135 |
|
✗ |
d->Ru.col(iu) = (d->data_u[iu]->r - d->data_0->r) / d->uh_jac; |
| 136 |
|
|
} |
| 137 |
|
|
// constraint |
| 138 |
|
✗ |
d->Gu.col(iu) = (d->data_u[iu]->g - g0) / d->uh_jac; |
| 139 |
|
✗ |
d->Hu.col(iu) = (d->data_u[iu]->h - h0) / d->uh_jac; |
| 140 |
|
✗ |
d->du(iu) = Scalar(0.); |
| 141 |
|
|
} |
| 142 |
|
✗ |
data->Fu /= d->uh_jac; |
| 143 |
|
|
|
| 144 |
|
|
#ifdef NDEBUG |
| 145 |
|
|
// Computing the d^2 cost(x,u) / dx^2 |
| 146 |
|
|
d->xh_hess = e_hess_ * std::max(Scalar(1.), d->x_norm); |
| 147 |
|
|
d->xh_hess_pow2 = d->xh_hess * d->xh_hess; |
| 148 |
|
|
for (std::size_t ix = 0; ix < ndx; ++ix) { |
| 149 |
|
|
d->dx(ix) = d->xh_hess; |
| 150 |
|
|
model_->get_state()->integrate(x, d->dx, d->xp); |
| 151 |
|
|
model_->calc(d->data_x[ix], d->xp, u); |
| 152 |
|
|
const Scalar cp = d->data_x[ix]->cost; |
| 153 |
|
|
model_->get_state()->integrate(x, -d->dx, d->xp); |
| 154 |
|
|
model_->calc(d->data_x[ix], d->xp, u); |
| 155 |
|
|
const Scalar cm = d->data_x[ix]->cost; |
| 156 |
|
|
data->Lxx(ix, ix) = (cp - 2 * c0 + cm) / d->xh_hess_pow2; |
| 157 |
|
|
for (std::size_t jx = ix + 1; jx < ndx; ++jx) { |
| 158 |
|
|
d->dx(jx) = d->xh_hess; |
| 159 |
|
|
model_->get_state()->integrate(x, d->dx, d->xp); |
| 160 |
|
|
model_->calc(d->data_x[ix], d->xp, u); |
| 161 |
|
|
const Scalar cpp = |
| 162 |
|
|
d->data_x[ix] |
| 163 |
|
|
->cost; // cost due to positive disturbance in both directions |
| 164 |
|
|
d->dx(ix) = Scalar(0.); |
| 165 |
|
|
model_->get_state()->integrate(x, d->dx, d->xp); |
| 166 |
|
|
model_->calc(d->data_x[ix], d->xp, u); |
| 167 |
|
|
const Scalar czp = |
| 168 |
|
|
d->data_x[ix]->cost; // cost due to zero disturance in 'i' and |
| 169 |
|
|
// positive disturbance in 'j' direction |
| 170 |
|
|
data->Lxx(ix, jx) = (cpp - czp - cp + c0) / d->xh_hess_pow2; |
| 171 |
|
|
data->Lxx(jx, ix) = data->Lxx(ix, jx); |
| 172 |
|
|
d->dx(ix) = d->xh_hess; |
| 173 |
|
|
d->dx(jx) = Scalar(0.); |
| 174 |
|
|
} |
| 175 |
|
|
d->dx(ix) = Scalar(0.); |
| 176 |
|
|
} |
| 177 |
|
|
|
| 178 |
|
|
// Computing the d^2 cost(x,u) / du^2 |
| 179 |
|
|
d->uh_hess = e_hess_ * std::max(Scalar(1.), u.norm()); |
| 180 |
|
|
d->uh_hess_pow2 = d->uh_hess * d->uh_hess; |
| 181 |
|
|
for (std::size_t iu = 0; iu < nu; ++iu) { |
| 182 |
|
|
d->du(iu) = d->uh_hess; |
| 183 |
|
|
model_->calc(d->data_u[iu], x, u + d->du); |
| 184 |
|
|
const Scalar cp = d->data_u[iu]->cost; |
| 185 |
|
|
model_->calc(d->data_u[iu], x, u - d->du); |
| 186 |
|
|
const Scalar cm = d->data_u[iu]->cost; |
| 187 |
|
|
data->Luu(iu, iu) = (cp - 2 * c0 + cm) / d->uh_hess_pow2; |
| 188 |
|
|
for (std::size_t ju = iu + 1; ju < nu; ++ju) { |
| 189 |
|
|
d->du(ju) = d->uh_hess; |
| 190 |
|
|
model_->calc(d->data_u[iu], x, u + d->du); |
| 191 |
|
|
const Scalar cpp = |
| 192 |
|
|
d->data_u[iu] |
| 193 |
|
|
->cost; // cost due to positive disturbance in both directions |
| 194 |
|
|
d->du(iu) = Scalar(0.); |
| 195 |
|
|
model_->calc(d->data_u[iu], x, u + d->du); |
| 196 |
|
|
const Scalar czp = |
| 197 |
|
|
d->data_u[iu]->cost; // cost due to zero disturance in 'i' and |
| 198 |
|
|
// positive disturbance in 'j' direction |
| 199 |
|
|
data->Luu(iu, ju) = (cpp - czp - cp + c0) / d->uh_hess_pow2; |
| 200 |
|
|
data->Luu(ju, iu) = data->Luu(iu, ju); |
| 201 |
|
|
d->du(iu) = d->uh_hess; |
| 202 |
|
|
d->du(ju) = Scalar(0.); |
| 203 |
|
|
} |
| 204 |
|
|
d->du(iu) = Scalar(0.); |
| 205 |
|
|
} |
| 206 |
|
|
|
| 207 |
|
|
// Computing the d^2 cost(x,u) / dxu |
| 208 |
|
|
d->xuh_hess_pow2 = Scalar(4.) * d->xh_hess * d->uh_hess; |
| 209 |
|
|
for (std::size_t ix = 0; ix < ndx; ++ix) { |
| 210 |
|
|
for (std::size_t ju = 0; ju < nu; ++ju) { |
| 211 |
|
|
d->dx(ix) = d->xh_hess; |
| 212 |
|
|
model_->get_state()->integrate(x, d->dx, d->xp); |
| 213 |
|
|
d->du(ju) = d->uh_hess; |
| 214 |
|
|
model_->calc(d->data_x[ix], d->xp, u + d->du); |
| 215 |
|
|
const Scalar cpp = d->data_x[ix]->cost; |
| 216 |
|
|
model_->calc(d->data_x[ix], d->xp, u - d->du); |
| 217 |
|
|
const Scalar cpm = d->data_x[ix]->cost; |
| 218 |
|
|
model_->get_state()->integrate(x, -d->dx, d->xp); |
| 219 |
|
|
model_->calc(d->data_x[ix], d->xp, u + d->du); |
| 220 |
|
|
const Scalar cmp = d->data_x[ix]->cost; |
| 221 |
|
|
model_->calc(d->data_x[ix], d->xp, u - d->du); |
| 222 |
|
|
const Scalar cmm = d->data_x[ix]->cost; |
| 223 |
|
|
data->Lxu(ix, ju) = (cpp - cpm - cmp + cmm) / d->xuh_hess_pow2; |
| 224 |
|
|
d->dx(ix) = Scalar(0.); |
| 225 |
|
|
d->du(ju) = Scalar(0.); |
| 226 |
|
|
} |
| 227 |
|
|
} |
| 228 |
|
|
#endif |
| 229 |
|
|
|
| 230 |
|
✗ |
if (get_with_gauss_approx()) { |
| 231 |
|
✗ |
data->Lxx = d->Rx.transpose() * d->Rx; |
| 232 |
|
✗ |
data->Lxu = d->Rx.transpose() * d->Ru; |
| 233 |
|
✗ |
data->Luu = d->Ru.transpose() * d->Ru; |
| 234 |
|
|
} |
| 235 |
|
|
} |
| 236 |
|
|
|
| 237 |
|
|
template <typename Scalar> |
| 238 |
|
✗ |
void ActionModelNumDiffTpl<Scalar>::calcDiff( |
| 239 |
|
|
const std::shared_ptr<ActionDataAbstract>& data, |
| 240 |
|
|
const Eigen::Ref<const VectorXs>& x) { |
| 241 |
|
✗ |
if (static_cast<std::size_t>(x.size()) != state_->get_nx()) { |
| 242 |
|
✗ |
throw_pretty( |
| 243 |
|
|
"Invalid argument: " << "x has wrong dimension (it should be " + |
| 244 |
|
|
std::to_string(state_->get_nx()) + ")"); |
| 245 |
|
|
} |
| 246 |
|
✗ |
Data* d = static_cast<Data*>(data.get()); |
| 247 |
|
|
|
| 248 |
|
✗ |
const Scalar c0 = d->data_0->cost; |
| 249 |
|
✗ |
data->xnext = d->data_0->xnext; |
| 250 |
|
✗ |
data->cost = d->data_0->cost; |
| 251 |
|
✗ |
const VectorXs& g0 = d->g; |
| 252 |
|
✗ |
const VectorXs& h0 = d->h; |
| 253 |
|
✗ |
const std::size_t ndx = model_->get_state()->get_ndx(); |
| 254 |
|
✗ |
d->Gx.conservativeResize(model_->get_ng_T(), ndx); |
| 255 |
|
✗ |
d->Hx.conservativeResize(model_->get_nh_T(), ndx); |
| 256 |
|
|
|
| 257 |
|
✗ |
assertStableStateFD(x); |
| 258 |
|
|
|
| 259 |
|
|
// Computing the d action(x,u) / dx |
| 260 |
|
✗ |
model_->get_state()->diff(model_->get_state()->zero(), x, d->dx); |
| 261 |
|
✗ |
d->x_norm = d->dx.norm(); |
| 262 |
|
✗ |
d->dx.setZero(); |
| 263 |
|
✗ |
d->xh_jac = e_jac_ * std::max(Scalar(1.), d->x_norm); |
| 264 |
|
✗ |
for (std::size_t ix = 0; ix < state_->get_ndx(); ++ix) { |
| 265 |
|
✗ |
d->dx(ix) = d->xh_jac; |
| 266 |
|
✗ |
model_->get_state()->integrate(x, d->dx, d->xp); |
| 267 |
|
✗ |
model_->calc(d->data_x[ix], d->xp); |
| 268 |
|
|
// cost |
| 269 |
|
✗ |
data->Lx(ix) = (d->data_x[ix]->cost - c0) / d->xh_jac; |
| 270 |
|
✗ |
if (get_with_gauss_approx()) { |
| 271 |
|
✗ |
d->Rx.col(ix) = (d->data_x[ix]->r - d->data_0->r) / d->xh_jac; |
| 272 |
|
|
} |
| 273 |
|
|
// constraint |
| 274 |
|
✗ |
d->Gx.col(ix) = (d->data_x[ix]->g - g0) / d->xh_jac; |
| 275 |
|
✗ |
d->Hx.col(ix) = (d->data_x[ix]->h - h0) / d->xh_jac; |
| 276 |
|
✗ |
d->dx(ix) = Scalar(0.); |
| 277 |
|
|
} |
| 278 |
|
|
|
| 279 |
|
|
#ifdef NDEBUG |
| 280 |
|
|
// Computing the d^2 cost(x,u) / dx^2 |
| 281 |
|
|
d->xh_hess = e_hess_ * std::max(Scalar(1.), d->x_norm); |
| 282 |
|
|
d->xh_hess_pow2 = d->xh_hess * d->xh_hess; |
| 283 |
|
|
for (std::size_t ix = 0; ix < ndx; ++ix) { |
| 284 |
|
|
// We can apply the same formulas for finite difference as above |
| 285 |
|
|
d->dx(ix) = d->xh_hess; |
| 286 |
|
|
model_->get_state()->integrate(x, d->dx, d->xp); |
| 287 |
|
|
model_->calc(d->data_x[ix], d->xp); |
| 288 |
|
|
const Scalar cp = d->data_x[ix]->cost; |
| 289 |
|
|
model_->get_state()->integrate(x, -d->dx, d->xp); |
| 290 |
|
|
model_->calc(d->data_x[ix], d->xp); |
| 291 |
|
|
const Scalar cm = d->data_x[ix]->cost; |
| 292 |
|
|
data->Lxx(ix, ix) = (cp - 2 * c0 + cm) / d->xh_hess_pow2; |
| 293 |
|
|
for (std::size_t jx = ix + 1; jx < ndx; ++jx) { |
| 294 |
|
|
d->dx(jx) = d->xh_hess; |
| 295 |
|
|
model_->get_state()->integrate(x, d->dx, d->xp); |
| 296 |
|
|
model_->calc(d->data_x[ix], d->xp); |
| 297 |
|
|
const Scalar cpp = |
| 298 |
|
|
d->data_x[ix] |
| 299 |
|
|
->cost; // cost due to positive disturbance in both directions |
| 300 |
|
|
d->dx(ix) = Scalar(0.); |
| 301 |
|
|
model_->get_state()->integrate(x, d->dx, d->xp); |
| 302 |
|
|
model_->calc(d->data_x[ix], d->xp); |
| 303 |
|
|
const Scalar czp = |
| 304 |
|
|
d->data_x[ix]->cost; // cost due to zero disturance in 'i' and |
| 305 |
|
|
// positive disturbance in 'j' direction |
| 306 |
|
|
data->Lxx(ix, jx) = (cpp - czp - cp + c0) / d->xh_hess_pow2; |
| 307 |
|
|
data->Lxx(jx, ix) = data->Lxx(ix, jx); |
| 308 |
|
|
d->dx(ix) = d->xh_hess; |
| 309 |
|
|
d->dx(jx) = Scalar(0.); |
| 310 |
|
|
} |
| 311 |
|
|
d->dx(ix) = Scalar(0.); |
| 312 |
|
|
} |
| 313 |
|
|
#endif |
| 314 |
|
|
|
| 315 |
|
✗ |
if (get_with_gauss_approx()) { |
| 316 |
|
✗ |
data->Lxx = d->Rx.transpose() * d->Rx; |
| 317 |
|
|
} |
| 318 |
|
|
} |
| 319 |
|
|
|
| 320 |
|
|
template <typename Scalar> |
| 321 |
|
|
std::shared_ptr<ActionDataAbstractTpl<Scalar> > |
| 322 |
|
✗ |
ActionModelNumDiffTpl<Scalar>::createData() { |
| 323 |
|
✗ |
return std::allocate_shared<Data>(Eigen::aligned_allocator<Data>(), this); |
| 324 |
|
|
} |
| 325 |
|
|
|
| 326 |
|
|
template <typename Scalar> |
| 327 |
|
✗ |
void ActionModelNumDiffTpl<Scalar>::quasiStatic( |
| 328 |
|
|
const std::shared_ptr<ActionDataAbstract>& data, Eigen::Ref<VectorXs> u, |
| 329 |
|
|
const Eigen::Ref<const VectorXs>& x, const std::size_t maxiter, |
| 330 |
|
|
const Scalar tol) { |
| 331 |
|
✗ |
Data* d = static_cast<Data*>(data.get()); |
| 332 |
|
✗ |
model_->quasiStatic(d->data_0, u, x, maxiter, tol); |
| 333 |
|
|
} |
| 334 |
|
|
|
| 335 |
|
|
template <typename Scalar> |
| 336 |
|
|
template <typename NewScalar> |
| 337 |
|
✗ |
ActionModelNumDiffTpl<NewScalar> ActionModelNumDiffTpl<Scalar>::cast() const { |
| 338 |
|
|
typedef ActionModelNumDiffTpl<NewScalar> ReturnType; |
| 339 |
|
✗ |
ReturnType res(model_->template cast<NewScalar>()); |
| 340 |
|
✗ |
return res; |
| 341 |
|
|
} |
| 342 |
|
|
|
| 343 |
|
|
template <typename Scalar> |
| 344 |
|
|
const std::shared_ptr<ActionModelAbstractTpl<Scalar> >& |
| 345 |
|
✗ |
ActionModelNumDiffTpl<Scalar>::get_model() const { |
| 346 |
|
✗ |
return model_; |
| 347 |
|
|
} |
| 348 |
|
|
|
| 349 |
|
|
template <typename Scalar> |
| 350 |
|
✗ |
const Scalar ActionModelNumDiffTpl<Scalar>::get_disturbance() const { |
| 351 |
|
✗ |
return e_jac_; |
| 352 |
|
|
} |
| 353 |
|
|
|
| 354 |
|
|
template <typename Scalar> |
| 355 |
|
✗ |
void ActionModelNumDiffTpl<Scalar>::set_disturbance(const Scalar disturbance) { |
| 356 |
|
✗ |
if (disturbance < Scalar(0.)) { |
| 357 |
|
✗ |
throw_pretty("Invalid argument: " << "Disturbance constant is positive"); |
| 358 |
|
|
} |
| 359 |
|
✗ |
e_jac_ = disturbance; |
| 360 |
|
✗ |
e_hess_ = sqrt(Scalar(2.0) * e_jac_); |
| 361 |
|
|
} |
| 362 |
|
|
|
| 363 |
|
|
template <typename Scalar> |
| 364 |
|
✗ |
bool ActionModelNumDiffTpl<Scalar>::get_with_gauss_approx() { |
| 365 |
|
✗ |
return with_gauss_approx_; |
| 366 |
|
|
} |
| 367 |
|
|
|
| 368 |
|
|
template <typename Scalar> |
| 369 |
|
✗ |
void ActionModelNumDiffTpl<Scalar>::print(std::ostream& os) const { |
| 370 |
|
✗ |
os << "ActionModelNumDiffTpl {action=" << *model_ << "}"; |
| 371 |
|
|
} |
| 372 |
|
|
|
| 373 |
|
|
template <typename Scalar> |
| 374 |
|
✗ |
void ActionModelNumDiffTpl<Scalar>::assertStableStateFD( |
| 375 |
|
|
const Eigen::Ref<const VectorXs>& /** x */) { |
| 376 |
|
|
// do nothing in the general case |
| 377 |
|
|
} |
| 378 |
|
|
|
| 379 |
|
|
} // namespace crocoddyl |
| 380 |
|
|
|