| Directory: | ./ |
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| File: | include/crocoddyl/core/action-base.hpp |
| Date: | 2025-03-26 19:23:43 |
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| Branches: | 86 | 186 | 46.2% |
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| 1 | /////////////////////////////////////////////////////////////////////////////// | ||
| 2 | // BSD 3-Clause License | ||
| 3 | // | ||
| 4 | // Copyright (C) 2019-2025, LAAS-CNRS, University of Edinburgh, | ||
| 5 | // University of Oxford, Heriot-Watt University | ||
| 6 | // Copyright note valid unless otherwise stated in individual files. | ||
| 7 | // All rights reserved. | ||
| 8 | /////////////////////////////////////////////////////////////////////////////// | ||
| 9 | |||
| 10 | #ifndef CROCODDYL_CORE_ACTION_BASE_HPP_ | ||
| 11 | #define CROCODDYL_CORE_ACTION_BASE_HPP_ | ||
| 12 | |||
| 13 | #include <memory> | ||
| 14 | #include <stdexcept> | ||
| 15 | |||
| 16 | #include "crocoddyl/core/fwd.hpp" | ||
| 17 | #include "crocoddyl/core/state-base.hpp" | ||
| 18 | |||
| 19 | namespace crocoddyl { | ||
| 20 | |||
| 21 | class ActionModelBase { | ||
| 22 | public: | ||
| 23 | 3404 | virtual ~ActionModelBase() = default; | |
| 24 | |||
| 25 | ✗ | CROCODDYL_BASE_CAST(ActionModelBase, ActionModelAbstractTpl) | |
| 26 | }; | ||
| 27 | |||
| 28 | /** | ||
| 29 | * @brief Abstract class for action model | ||
| 30 | * | ||
| 31 | * An action model combines dynamics, cost functions and constraints. Each node, | ||
| 32 | * in our optimal control problem, is described through an action model. Every | ||
| 33 | * time that we want describe a problem, we need to provide ways of computing | ||
| 34 | * the dynamics, cost functions, constraints and their derivatives. All these is | ||
| 35 | * described inside the action model. | ||
| 36 | * | ||
| 37 | * Concretely speaking, the action model describes a time-discrete action model | ||
| 38 | * with a first-order ODE along a cost function, i.e. | ||
| 39 | * - the state \f$\mathbf{z}\in\mathcal{Z}\f$ lies in a manifold described with | ||
| 40 | * a `nx`-tuple, | ||
| 41 | * - the state rate \f$\mathbf{\dot{x}}\in T_{\mathbf{q}}\mathcal{Q}\f$ is the | ||
| 42 | * tangent vector to the state manifold with `ndx` dimension, | ||
| 43 | * - the control input \f$\mathbf{u}\in\mathbb{R}^{nu}\f$ is an Euclidean | ||
| 44 | * vector | ||
| 45 | * - \f$\mathbf{r}(\cdot)\f$ and \f$a(\cdot)\f$ are the residual and activation | ||
| 46 | * functions (see `ResidualModelAbstractTpl` and `ActivationModelAbstractTpl`, | ||
| 47 | * respetively), | ||
| 48 | * - \f$\mathbf{g}(\cdot)\in\mathbb{R}^{ng}\f$ and | ||
| 49 | * \f$\mathbf{h}(\cdot)\in\mathbb{R}^{nh}\f$ are the inequality and equality | ||
| 50 | * vector functions, respectively. | ||
| 51 | * | ||
| 52 | * The computation of these equations are carried out out inside `calc()` | ||
| 53 | * function. In short, this function computes the system acceleration, cost and | ||
| 54 | * constraints values (also called constraints violations). This procedure is | ||
| 55 | * equivalent to running a forward pass of the action model. | ||
| 56 | * | ||
| 57 | * However, during numerical optimization, we also need to run backward passes | ||
| 58 | * of the action model. These calculations are performed by `calcDiff()`. In | ||
| 59 | * short, this method builds a linear-quadratic approximation of the action | ||
| 60 | * model, i.e.: \f[ \begin{aligned} | ||
| 61 | * &\delta\mathbf{x}_{k+1} = | ||
| 62 | * \mathbf{f_x}\delta\mathbf{x}_k+\mathbf{f_u}\delta\mathbf{u}_k, | ||
| 63 | * &\textrm{(dynamics)}\\ | ||
| 64 | * &\ell(\delta\mathbf{x}_k,\delta\mathbf{u}_k) = \begin{bmatrix}1 | ||
| 65 | * \\ \delta\mathbf{x}_k \\ \delta\mathbf{u}_k\end{bmatrix}^T \begin{bmatrix}0 & | ||
| 66 | * \mathbf{\ell_x}^T & \mathbf{\ell_u}^T \\ \mathbf{\ell_x} & \mathbf{\ell_{xx}} | ||
| 67 | * & | ||
| 68 | * \mathbf{\ell_{ux}}^T \\ | ||
| 69 | * \mathbf{\ell_u} & \mathbf{\ell_{ux}} & \mathbf{\ell_{uu}}\end{bmatrix} | ||
| 70 | * \begin{bmatrix}1 \\ \delta\mathbf{x}_k \\ | ||
| 71 | * \delta\mathbf{u}_k\end{bmatrix}, &\textrm{(cost)}\\ | ||
| 72 | * &\mathbf{g}(\delta\mathbf{x}_k,\delta\mathbf{u}_k)<\mathbf{0}, | ||
| 73 | * &\textrm{(inequality constraint)}\\ | ||
| 74 | * &\mathbf{h}(\delta\mathbf{x}_k,\delta\mathbf{u}_k)=\mathbf{0}, | ||
| 75 | * &\textrm{(equality constraint)} \end{aligned} \f] where | ||
| 76 | * - \f$\mathbf{f_x}\in\mathbb{R}^{ndx\times ndx}\f$ and | ||
| 77 | * \f$\mathbf{f_u}\in\mathbb{R}^{ndx\times nu}\f$ are the Jacobians of the | ||
| 78 | * dynamics, | ||
| 79 | * - \f$\mathbf{\ell_x}\in\mathbb{R}^{ndx}\f$ and | ||
| 80 | * \f$\mathbf{\ell_u}\in\mathbb{R}^{nu}\f$ are the Jacobians of the cost | ||
| 81 | * function, | ||
| 82 | * - \f$\mathbf{\ell_{xx}}\in\mathbb{R}^{ndx\times ndx}\f$, | ||
| 83 | * \f$\mathbf{\ell_{xu}}\in\mathbb{R}^{ndx\times nu}\f$ and | ||
| 84 | * \f$\mathbf{\ell_{uu}}\in\mathbb{R}^{nu\times nu}\f$ are the Hessians of the | ||
| 85 | * cost function, | ||
| 86 | * - \f$\mathbf{g_x}\in\mathbb{R}^{ng\times ndx}\f$ and | ||
| 87 | * \f$\mathbf{g_u}\in\mathbb{R}^{ng\times nu}\f$ are the Jacobians of the | ||
| 88 | * inequality constraints, and | ||
| 89 | * - \f$\mathbf{h_x}\in\mathbb{R}^{nh\times ndx}\f$ and | ||
| 90 | * \f$\mathbf{h_u}\in\mathbb{R}^{nh\times nu}\f$ are the Jacobians of the | ||
| 91 | * equality constraints. | ||
| 92 | * | ||
| 93 | * Additionally, it is important to note that `calcDiff()` computes the | ||
| 94 | * derivatives using the latest stored values by `calc()`. Thus, we need to | ||
| 95 | * first run `calc()`. | ||
| 96 | * | ||
| 97 | * \sa `calc()`, `calcDiff()`, `createData()` | ||
| 98 | */ | ||
| 99 | template <typename _Scalar> | ||
| 100 | class ActionModelAbstractTpl : public ActionModelBase { | ||
| 101 | public: | ||
| 102 | EIGEN_MAKE_ALIGNED_OPERATOR_NEW | ||
| 103 | |||
| 104 | typedef _Scalar Scalar; | ||
| 105 | typedef typename ScalarSelector<Scalar>::type ScalarType; | ||
| 106 | typedef MathBaseTpl<Scalar> MathBase; | ||
| 107 | typedef ActionDataAbstractTpl<Scalar> ActionDataAbstract; | ||
| 108 | typedef StateAbstractTpl<Scalar> StateAbstract; | ||
| 109 | typedef typename MathBase::VectorXs VectorXs; | ||
| 110 | |||
| 111 | /** | ||
| 112 | * @brief Initialize the action model | ||
| 113 | * | ||
| 114 | * @param[in] state State description | ||
| 115 | * @param[in] nu Dimension of control vector | ||
| 116 | * @param[in] nr Dimension of cost-residual vector | ||
| 117 | * @param[in] ng Number of inequality constraints (default 0) | ||
| 118 | * @param[in] nh Number of equality constraints (default 0) | ||
| 119 | * @param[in] ng_T Number of inequality terminal constraints (default 0) | ||
| 120 | * @param[in] nh_T Number of equality terminal constraints (default 0) | ||
| 121 | */ | ||
| 122 | ActionModelAbstractTpl(std::shared_ptr<StateAbstract> state, | ||
| 123 | const std::size_t nu, const std::size_t nr = 0, | ||
| 124 | const std::size_t ng = 0, const std::size_t nh = 0, | ||
| 125 | const std::size_t ng_T = 0, | ||
| 126 | const std::size_t nh_T = 0); | ||
| 127 | /** | ||
| 128 | * @brief Copy constructor | ||
| 129 | * @param other Action model to be copied | ||
| 130 | */ | ||
| 131 | ActionModelAbstractTpl(const ActionModelAbstractTpl<Scalar>& other); | ||
| 132 | |||
| 133 | 3404 | virtual ~ActionModelAbstractTpl() = default; | |
| 134 | |||
| 135 | /** | ||
| 136 | * @brief Compute the next state and cost value | ||
| 137 | * | ||
| 138 | * @param[in] data Action data | ||
| 139 | * @param[in] x State point \f$\mathbf{x}\in\mathbb{R}^{ndx}\f$ | ||
| 140 | * @param[in] u Control input \f$\mathbf{u}\in\mathbb{R}^{nu}\f$ | ||
| 141 | */ | ||
| 142 | virtual void calc(const std::shared_ptr<ActionDataAbstract>& data, | ||
| 143 | const Eigen::Ref<const VectorXs>& x, | ||
| 144 | const Eigen::Ref<const VectorXs>& u) = 0; | ||
| 145 | |||
| 146 | /** | ||
| 147 | * @brief Compute the total cost value for nodes that depends only on the | ||
| 148 | * state | ||
| 149 | * | ||
| 150 | * It updates the total cost and the next state is not computed as it is not | ||
| 151 | * expected to change. This function is used in the terminal nodes of an | ||
| 152 | * optimal control problem. | ||
| 153 | * | ||
| 154 | * @param[in] data Action data | ||
| 155 | * @param[in] x State point \f$\mathbf{x}\in\mathbb{R}^{ndx}\f$ | ||
| 156 | */ | ||
| 157 | virtual void calc(const std::shared_ptr<ActionDataAbstract>& data, | ||
| 158 | const Eigen::Ref<const VectorXs>& x); | ||
| 159 | |||
| 160 | /** | ||
| 161 | * @brief Compute the derivatives of the dynamics and cost functions | ||
| 162 | * | ||
| 163 | * It computes the partial derivatives of the dynamical system and the cost | ||
| 164 | * function. It assumes that `calc()` has been run first. This function builds | ||
| 165 | * a linear-quadratic approximation of the action model (i.e. dynamical system | ||
| 166 | * and cost function). | ||
| 167 | * | ||
| 168 | * @param[in] data Action data | ||
| 169 | * @param[in] x State point \f$\mathbf{x}\in\mathbb{R}^{ndx}\f$ | ||
| 170 | * @param[in] u Control input \f$\mathbf{u}\in\mathbb{R}^{nu}\f$ | ||
| 171 | */ | ||
| 172 | virtual void calcDiff(const std::shared_ptr<ActionDataAbstract>& data, | ||
| 173 | const Eigen::Ref<const VectorXs>& x, | ||
| 174 | const Eigen::Ref<const VectorXs>& u) = 0; | ||
| 175 | |||
| 176 | /** | ||
| 177 | * @brief Compute the derivatives of the cost functions with respect to the | ||
| 178 | * state only | ||
| 179 | * | ||
| 180 | * It updates the derivatives of the cost function with respect to the state | ||
| 181 | * only. This function is used in the terminal nodes of an optimal control | ||
| 182 | * problem. | ||
| 183 | * | ||
| 184 | * @param[in] data Action data | ||
| 185 | * @param[in] x State point \f$\mathbf{x}\in\mathbb{R}^{ndx}\f$ | ||
| 186 | */ | ||
| 187 | virtual void calcDiff(const std::shared_ptr<ActionDataAbstract>& data, | ||
| 188 | const Eigen::Ref<const VectorXs>& x); | ||
| 189 | |||
| 190 | /** | ||
| 191 | * @brief Create the action data | ||
| 192 | * | ||
| 193 | * @return the action data | ||
| 194 | */ | ||
| 195 | virtual std::shared_ptr<ActionDataAbstract> createData(); | ||
| 196 | |||
| 197 | /** | ||
| 198 | * @brief Checks that a specific data belongs to this model | ||
| 199 | */ | ||
| 200 | virtual bool checkData(const std::shared_ptr<ActionDataAbstract>& data); | ||
| 201 | |||
| 202 | /** | ||
| 203 | * @brief Computes the quasic static commands | ||
| 204 | * | ||
| 205 | * The quasic static commands are the ones produced for a the reference | ||
| 206 | * posture as an equilibrium point, i.e. for | ||
| 207 | * \f$\mathbf{f^q_x}\delta\mathbf{q}+\mathbf{f_u}\delta\mathbf{u}=\mathbf{0}\f$ | ||
| 208 | * | ||
| 209 | * @param[in] data Action data | ||
| 210 | * @param[out] u Quasic static commands | ||
| 211 | * @param[in] x State point (velocity has to be zero) | ||
| 212 | * @param[in] maxiter Maximum allowed number of iterations | ||
| 213 | * @param[in] tol Tolerance | ||
| 214 | */ | ||
| 215 | virtual void quasiStatic(const std::shared_ptr<ActionDataAbstract>& data, | ||
| 216 | Eigen::Ref<VectorXs> u, | ||
| 217 | const Eigen::Ref<const VectorXs>& x, | ||
| 218 | const std::size_t maxiter = 100, | ||
| 219 | const Scalar tol = Scalar(1e-9)); | ||
| 220 | |||
| 221 | /** | ||
| 222 | * @copybrief quasicStatic() | ||
| 223 | * | ||
| 224 | * @copydetails quasicStatic() | ||
| 225 | * | ||
| 226 | * @param[in] data Action data | ||
| 227 | * @param[in] x State point (velocity has to be zero) | ||
| 228 | * @param[in] maxiter Maximum allowed number of iterations | ||
| 229 | * @param[in] tol Tolerance | ||
| 230 | * @return Quasic static commands | ||
| 231 | */ | ||
| 232 | VectorXs quasiStatic_x(const std::shared_ptr<ActionDataAbstract>& data, | ||
| 233 | const VectorXs& x, const std::size_t maxiter = 100, | ||
| 234 | const Scalar tol = Scalar(1e-9)); | ||
| 235 | |||
| 236 | /** | ||
| 237 | * @brief Return the dimension of the control input | ||
| 238 | */ | ||
| 239 | std::size_t get_nu() const; | ||
| 240 | |||
| 241 | /** | ||
| 242 | * @brief Return the dimension of the cost-residual vector | ||
| 243 | */ | ||
| 244 | std::size_t get_nr() const; | ||
| 245 | |||
| 246 | /** | ||
| 247 | * @brief Return the number of inequality constraints | ||
| 248 | */ | ||
| 249 | virtual std::size_t get_ng() const; | ||
| 250 | |||
| 251 | /** | ||
| 252 | * @brief Return the number of equality constraints | ||
| 253 | */ | ||
| 254 | virtual std::size_t get_nh() const; | ||
| 255 | |||
| 256 | /** | ||
| 257 | * @brief Return the number of inequality terminal constraints | ||
| 258 | */ | ||
| 259 | virtual std::size_t get_ng_T() const; | ||
| 260 | |||
| 261 | /** | ||
| 262 | * @brief Return the number of equality terminal constraints | ||
| 263 | */ | ||
| 264 | virtual std::size_t get_nh_T() const; | ||
| 265 | |||
| 266 | /** | ||
| 267 | * @brief Return the state | ||
| 268 | */ | ||
| 269 | const std::shared_ptr<StateAbstract>& get_state() const; | ||
| 270 | |||
| 271 | /** | ||
| 272 | * @brief Return the lower bound of the inequality constraints | ||
| 273 | */ | ||
| 274 | virtual const VectorXs& get_g_lb() const; | ||
| 275 | |||
| 276 | /** | ||
| 277 | * @brief Return the upper bound of the inequality constraints | ||
| 278 | */ | ||
| 279 | virtual const VectorXs& get_g_ub() const; | ||
| 280 | |||
| 281 | /** | ||
| 282 | * @brief Return the control lower bound | ||
| 283 | */ | ||
| 284 | const VectorXs& get_u_lb() const; | ||
| 285 | |||
| 286 | /** | ||
| 287 | * @brief Return the control upper bound | ||
| 288 | */ | ||
| 289 | const VectorXs& get_u_ub() const; | ||
| 290 | |||
| 291 | /** | ||
| 292 | * @brief Indicates if there are defined control limits | ||
| 293 | */ | ||
| 294 | bool get_has_control_limits() const; | ||
| 295 | |||
| 296 | /** | ||
| 297 | * @brief Modify the lower bound of the inequality constraints | ||
| 298 | */ | ||
| 299 | void set_g_lb(const VectorXs& g_lb); | ||
| 300 | |||
| 301 | /** | ||
| 302 | * @brief Modify the upper bound of the inequality constraints | ||
| 303 | */ | ||
| 304 | void set_g_ub(const VectorXs& g_ub); | ||
| 305 | |||
| 306 | /** | ||
| 307 | * @brief Modify the control lower bounds | ||
| 308 | */ | ||
| 309 | void set_u_lb(const VectorXs& u_lb); | ||
| 310 | |||
| 311 | /** | ||
| 312 | * @brief Modify the control upper bounds | ||
| 313 | */ | ||
| 314 | void set_u_ub(const VectorXs& u_ub); | ||
| 315 | |||
| 316 | /** | ||
| 317 | * @brief Print information on the action model | ||
| 318 | */ | ||
| 319 | template <class Scalar> | ||
| 320 | friend std::ostream& operator<<(std::ostream& os, | ||
| 321 | const ActionModelAbstractTpl<Scalar>& model); | ||
| 322 | |||
| 323 | /** | ||
| 324 | * @brief Print relevant information of the action model | ||
| 325 | * | ||
| 326 | * @param[out] os Output stream object | ||
| 327 | */ | ||
| 328 | virtual void print(std::ostream& os) const; | ||
| 329 | |||
| 330 | protected: | ||
| 331 | std::size_t nu_; //!< Control dimension | ||
| 332 | std::size_t nr_; //!< Dimension of the cost residual | ||
| 333 | std::size_t ng_; //!< Number of inequality constraints | ||
| 334 | std::size_t nh_; //!< Number of equality constraints | ||
| 335 | std::size_t ng_T_; //!< Number of inequality terminal constraints | ||
| 336 | std::size_t nh_T_; //!< Number of equality terminal constraints | ||
| 337 | std::shared_ptr<StateAbstract> state_; //!< Model of the state | ||
| 338 | VectorXs unone_; //!< Neutral state | ||
| 339 | VectorXs g_lb_; //!< Lower bound of the inequality constraints | ||
| 340 | VectorXs g_ub_; //!< Lower bound of the inequality constraints | ||
| 341 | VectorXs u_lb_; //!< Lower control limits | ||
| 342 | VectorXs u_ub_; //!< Upper control limits | ||
| 343 | bool has_control_limits_; //!< Indicates whether any of the control limits is | ||
| 344 | //!< finite | ||
| 345 | ✗ | ActionModelAbstractTpl() | |
| 346 | ✗ | : nu_(0), nr_(0), ng_(0), nh_(0), ng_T_(0), nh_T_(0), state_(nullptr) {} | |
| 347 | |||
| 348 | /** | ||
| 349 | * @brief Update the status of the control limits (i.e. if there are defined | ||
| 350 | * limits) | ||
| 351 | */ | ||
| 352 | void update_has_control_limits(); | ||
| 353 | |||
| 354 | template <class Scalar> | ||
| 355 | friend class ConstraintModelManagerTpl; | ||
| 356 | }; | ||
| 357 | |||
| 358 | template <typename _Scalar> | ||
| 359 | struct ActionDataAbstractTpl { | ||
| 360 | EIGEN_MAKE_ALIGNED_OPERATOR_NEW | ||
| 361 | |||
| 362 | typedef _Scalar Scalar; | ||
| 363 | typedef MathBaseTpl<Scalar> MathBase; | ||
| 364 | typedef typename MathBase::VectorXs VectorXs; | ||
| 365 | typedef typename MathBase::MatrixXs MatrixXs; | ||
| 366 | |||
| 367 | template <template <typename Scalar> class Model> | ||
| 368 | 47742 | explicit ActionDataAbstractTpl(Model<Scalar>* const model) | |
| 369 | 47742 | : cost(Scalar(0.)), | |
| 370 |
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47742 | xnext(model->get_state()->get_nx()), |
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47742 | Fx(model->get_state()->get_ndx(), model->get_state()->get_ndx()), |
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47742 | Fu(model->get_state()->get_ndx(), model->get_nu()), |
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47742 | r(model->get_nr()), |
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47742 | Lx(model->get_state()->get_ndx()), |
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47742 | Lu(model->get_nu()), |
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47742 | Lxx(model->get_state()->get_ndx(), model->get_state()->get_ndx()), |
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47742 | Lxu(model->get_state()->get_ndx(), model->get_nu()), |
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47742 | Luu(model->get_nu(), model->get_nu()), |
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95484 | g(model->get_ng() > model->get_ng_T() ? model->get_ng() |
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47742 | : model->get_ng_T()), |
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95484 | Gx(model->get_ng() > model->get_ng_T() ? model->get_ng() |
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47742 | : model->get_ng_T(), |
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47742 | model->get_state()->get_ndx()), |
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95484 | Gu(model->get_ng() > model->get_ng_T() ? model->get_ng() |
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47742 | : model->get_ng_T(), |
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47742 | model->get_nu()), |
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82817 | h(model->get_nh() > model->get_nh_T() ? model->get_nh() |
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35075 | : model->get_nh_T()), |
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35075 | : model->get_nh_T(), |
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47742 | model->get_state()->get_ndx()), |
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82817 | Hu(model->get_nh() > model->get_nh_T() ? model->get_nh() |
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35075 | : model->get_nh_T(), |
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95484 | model->get_nu()) { |
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47742 | xnext.setZero(); |
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47742 | Hu.setZero(); |
| 410 | 47742 | } | |
| 411 | 91768 | virtual ~ActionDataAbstractTpl() = default; | |
| 412 | |||
| 413 | Scalar cost; //!< cost value | ||
| 414 | VectorXs xnext; //!< evolution state | ||
| 415 | MatrixXs Fx; //!< Jacobian of the dynamics w.r.t. the state \f$\mathbf{x}\f$ | ||
| 416 | MatrixXs | ||
| 417 | Fu; //!< Jacobian of the dynamics w.r.t. the control \f$\mathbf{u}\f$ | ||
| 418 | VectorXs r; //!< Cost residual | ||
| 419 | VectorXs Lx; //!< Jacobian of the cost w.r.t. the state \f$\mathbf{x}\f$ | ||
| 420 | VectorXs Lu; //!< Jacobian of the cost w.r.t. the control \f$\mathbf{u}\f$ | ||
| 421 | MatrixXs Lxx; //!< Hessian of the cost w.r.t. the state \f$\mathbf{x}\f$ | ||
| 422 | MatrixXs Lxu; //!< Hessian of the cost w.r.t. the state \f$\mathbf{x}\f$ and | ||
| 423 | //!< control \f$\mathbf{u}\f$ | ||
| 424 | MatrixXs Luu; //!< Hessian of the cost w.r.t. the control \f$\mathbf{u}\f$ | ||
| 425 | VectorXs g; //!< Inequality constraint values | ||
| 426 | MatrixXs Gx; //!< Jacobian of the inequality constraint w.r.t. the state | ||
| 427 | //!< \f$\mathbf{x}\f$ | ||
| 428 | MatrixXs Gu; //!< Jacobian of the inequality constraint w.r.t. the control | ||
| 429 | //!< \f$\mathbf{u}\f$ | ||
| 430 | VectorXs h; //!< Equality constraint values | ||
| 431 | MatrixXs Hx; //!< Jacobian of the equality constraint w.r.t. the state | ||
| 432 | //!< \f$\mathbf{x}\f$ | ||
| 433 | MatrixXs Hu; //!< Jacobian of the equality constraint w.r.t. the control | ||
| 434 | //!< \f$\mathbf{u}\f$ | ||
| 435 | }; | ||
| 436 | |||
| 437 | } // namespace crocoddyl | ||
| 438 | |||
| 439 | /* --- Details -------------------------------------------------------------- */ | ||
| 440 | /* --- Details -------------------------------------------------------------- */ | ||
| 441 | /* --- Details -------------------------------------------------------------- */ | ||
| 442 | #include "crocoddyl/core/action-base.hxx" | ||
| 443 | |||
| 444 | CROCODDYL_DECLARE_EXTERN_TEMPLATE_CLASS(crocoddyl::ActionModelAbstractTpl) | ||
| 445 | CROCODDYL_DECLARE_EXTERN_TEMPLATE_STRUCT(crocoddyl::ActionDataAbstractTpl) | ||
| 446 | |||
| 447 | #endif // CROCODDYL_CORE_ACTION_BASE_HPP_ | ||
| 448 |