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/////////////////////////////////////////////////////////////////////////////// |
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// BSD 3-Clause License |
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// |
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// Copyright (C) 2019-2022, LAAS-CNRS, University of Edinburgh, |
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// University of Oxford, 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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#ifndef CROCODDYL_CORE_SOLVERS_DDP_HPP_ |
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#define CROCODDYL_CORE_SOLVERS_DDP_HPP_ |
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#include <Eigen/Cholesky> |
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#include <vector> |
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#include "crocoddyl/core/mathbase.hpp" |
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#include "crocoddyl/core/solver-base.hpp" |
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#include "crocoddyl/core/utils/deprecate.hpp" |
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namespace crocoddyl { |
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/** |
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* @brief Differential Dynamic Programming (DDP) solver |
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* |
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* The DDP solver computes an optimal trajectory and control commands by |
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* iterates running `backwardPass()` and `forwardPass()`. The backward-pass |
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* updates locally the quadratic approximation of the problem and computes |
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* descent direction. If the warm-start is feasible, then it computes the gaps |
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* \f$\mathbf{f}_s\f$ and run a modified Riccati sweep: \f{eqnarray*} |
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* \mathbf{Q}_{\mathbf{x}_k} &=& \mathbf{l}_{\mathbf{x}_k} + |
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* \mathbf{f}^\top_{\mathbf{x}_k} (V_{\mathbf{x}_{k+1}} + |
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* V_{\mathbf{xx}_{k+1}}\mathbf{\bar{f}}_{k+1}),\\ |
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* \mathbf{Q}_{\mathbf{u}_k} &=& \mathbf{l}_{\mathbf{u}_k} + |
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* \mathbf{f}^\top_{\mathbf{u}_k} (V_{\mathbf{x}_{k+1}} + |
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* V_{\mathbf{xx}_{k+1}}\mathbf{\bar{f}}_{k+1}),\\ |
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* \mathbf{Q}_{\mathbf{xx}_k} &=& \mathbf{l}_{\mathbf{xx}_k} + |
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* \mathbf{f}^\top_{\mathbf{x}_k} V_{\mathbf{xx}_{k+1}} |
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* \mathbf{f}_{\mathbf{x}_k},\\ |
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* \mathbf{Q}_{\mathbf{xu}_k} &=& \mathbf{l}_{\mathbf{xu}_k} + |
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* \mathbf{f}^\top_{\mathbf{x}_k} V_{\mathbf{xx}_{k+1}} |
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* \mathbf{f}_{\mathbf{u}_k},\\ |
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* \mathbf{Q}_{\mathbf{uu}_k} &=& \mathbf{l}_{\mathbf{uu}_k} + |
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* \mathbf{f}^\top_{\mathbf{u}_k} V_{\mathbf{xx}_{k+1}} |
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* \mathbf{f}_{\mathbf{u}_k}. |
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* \f} |
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* Then, the forward-pass rollouts this new policy by integrating the system |
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* dynamics along a tuple of optimized control commands \f$\mathbf{u}^*_s\f$, |
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* i.e. \f{eqnarray} |
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* \mathbf{\hat{x}}_0 &=& \mathbf{\tilde{x}}_0,\\ |
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* \mathbf{\hat{u}}_k &=& \mathbf{u}_k + \alpha\mathbf{k}_k + |
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* \mathbf{K}_k(\mathbf{\hat{x}}_k-\mathbf{x}_k),\\ \mathbf{\hat{x}}_{k+1} &=& |
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* \mathbf{f}_k(\mathbf{\hat{x}}_k,\mathbf{\hat{u}}_k). \f} |
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* |
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* \sa SolverAbstract(), `backwardPass()` and `forwardPass()` |
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*/ |
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class SolverDDP : public SolverAbstract { |
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public: |
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EIGEN_MAKE_ALIGNED_OPERATOR_NEW |
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typedef typename MathBaseTpl<double>::MatrixXsRowMajor MatrixXdRowMajor; |
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/** |
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* @brief Initialize the DDP solver |
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* |
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* @param[in] problem shooting problem |
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*/ |
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explicit SolverDDP(boost::shared_ptr<ShootingProblem> problem); |
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virtual ~SolverDDP(); |
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virtual bool solve( |
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const std::vector<Eigen::VectorXd>& init_xs = DEFAULT_VECTOR, |
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const std::vector<Eigen::VectorXd>& init_us = DEFAULT_VECTOR, |
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const std::size_t maxiter = 100, const bool is_feasible = false, |
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const double init_reg = NAN); |
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virtual void computeDirection(const bool recalc = true); |
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virtual double tryStep(const double steplength = 1); |
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virtual double stoppingCriteria(); |
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virtual const Eigen::Vector2d& expectedImprovement(); |
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virtual void resizeData(); |
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/** |
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* @brief Update the Jacobian, Hessian and feasibility of the optimal control |
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* problem |
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* |
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* These derivatives are computed around the guess state and control |
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* trajectory. These trajectory can be set by using `setCandidate()`. |
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* |
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* @return the total cost around the guess trajectory |
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*/ |
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virtual double calcDiff(); |
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/** |
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* @brief Run the backward pass (Riccati sweep) |
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* |
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* It assumes that the Jacobian and Hessians of the optimal control problem |
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* have been compute (i.e., `calcDiff()`). The backward pass handles |
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* infeasible guess through a modified Riccati sweep: \f{eqnarray*} |
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* \mathbf{Q}_{\mathbf{x}_k} &=& \mathbf{l}_{\mathbf{x}_k} + |
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* \mathbf{f}^\top_{\mathbf{x}_k} (V_{\mathbf{x}_{k+1}} |
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* + |
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* V_{\mathbf{xx}_{k+1}}\mathbf{\bar{f}}_{k+1}),\\ |
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* \mathbf{Q}_{\mathbf{u}_k} &=& \mathbf{l}_{\mathbf{u}_k} + |
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* \mathbf{f}^\top_{\mathbf{u}_k} (V_{\mathbf{x}_{k+1}} |
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* + |
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* V_{\mathbf{xx}_{k+1}}\mathbf{\bar{f}}_{k+1}),\\ |
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* \mathbf{Q}_{\mathbf{xx}_k} &=& \mathbf{l}_{\mathbf{xx}_k} + |
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* \mathbf{f}^\top_{\mathbf{x}_k} V_{\mathbf{xx}_{k+1}} |
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* \mathbf{f}_{\mathbf{x}_k},\\ |
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* \mathbf{Q}_{\mathbf{xu}_k} &=& \mathbf{l}_{\mathbf{xu}_k} + |
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* \mathbf{f}^\top_{\mathbf{x}_k} V_{\mathbf{xx}_{k+1}} |
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* \mathbf{f}_{\mathbf{u}_k},\\ |
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* \mathbf{Q}_{\mathbf{uu}_k} &=& \mathbf{l}_{\mathbf{uu}_k} + |
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* \mathbf{f}^\top_{\mathbf{u}_k} V_{\mathbf{xx}_{k+1}} |
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* \mathbf{f}_{\mathbf{u}_k}, \f} where |
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* \f$\mathbf{l}_{\mathbf{x}_k}\f$,\f$\mathbf{l}_{\mathbf{u}_k}\f$,\f$\mathbf{f}_{\mathbf{x}_k}\f$ |
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* and \f$\mathbf{f}_{\mathbf{u}_k}\f$ are the Jacobians of the cost function |
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* and dynamics, |
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* \f$\mathbf{l}_{\mathbf{xx}_k}\f$,\f$\mathbf{l}_{\mathbf{xu}_k}\f$ and |
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* \f$\mathbf{l}_{\mathbf{uu}_k}\f$ are the Hessians of the cost function, |
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* \f$V_{\mathbf{x}_{k+1}}\f$ and \f$V_{\mathbf{xx}_{k+1}}\f$ defines the |
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* linear-quadratic approximation of the Value function, and |
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* \f$\mathbf{\bar{f}}_{k+1}\f$ describes the gaps of the dynamics. |
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*/ |
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virtual void backwardPass(); |
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/** |
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* @brief Run the forward pass or rollout |
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* |
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* It rollouts the action model given the computed policy (feedforward terns |
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* and feedback gains) by the `backwardPass()`: \f{eqnarray} |
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* \mathbf{\hat{x}}_0 &=& \mathbf{\tilde{x}}_0,\\ |
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* \mathbf{\hat{u}}_k &=& \mathbf{u}_k + \alpha\mathbf{k}_k + |
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* \mathbf{K}_k(\mathbf{\hat{x}}_k-\mathbf{x}_k),\\ \mathbf{\hat{x}}_{k+1} &=& |
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* \mathbf{f}_k(\mathbf{\hat{x}}_k,\mathbf{\hat{u}}_k). \f} We can define |
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* different step lengths \f$\alpha\f$. |
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* |
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* @param stepLength applied step length (\f$0\leq\alpha\leq1\f$) |
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*/ |
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virtual void forwardPass(const double stepLength); |
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/** |
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* @brief Compute the linear-quadratic approximation of the control |
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* Hamiltonian function |
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* |
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* @param[in] t Time instance |
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* @param[in] model Action model in the given time instance |
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* @param[in] data Action data in the given time instance |
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*/ |
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virtual void computeActionValueFunction( |
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const std::size_t t, const boost::shared_ptr<ActionModelAbstract>& model, |
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const boost::shared_ptr<ActionDataAbstract>& data); |
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/** |
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* @brief Compute the linear-quadratic approximation of the Value function |
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* |
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* This function is called in the backward pass after updating the local |
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* Hamiltonian. |
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* |
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* @param[in] t Time instance |
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* @param[in] model Action model in the given time instance |
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*/ |
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virtual void computeValueFunction( |
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const std::size_t t, const boost::shared_ptr<ActionModelAbstract>& model); |
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/** |
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* @brief Compute the feedforward and feedback terms using a Cholesky |
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* decomposition |
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* |
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* To compute the feedforward \f$\mathbf{k}_k\f$ and feedback |
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* \f$\mathbf{K}_k\f$ terms, we use a Cholesky decomposition to solve |
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* \f$\mathbf{Q}_{\mathbf{uu}_k}^{-1}\f$ term: \f{eqnarray} |
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* \mathbf{k}_k &=& \mathbf{Q}_{\mathbf{uu}_k}^{-1}\mathbf{Q}_{\mathbf{u}},\\ |
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* \mathbf{K}_k &=& \mathbf{Q}_{\mathbf{uu}_k}^{-1}\mathbf{Q}_{\mathbf{ux}}. |
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* \f} |
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* |
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* Note that if the Cholesky decomposition fails, then we re-start the |
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* backward pass and increase the state and control regularization values. |
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*/ |
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virtual void computeGains(const std::size_t t); |
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/** |
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* @brief Increase the state and control regularization values by a |
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* `regfactor_` factor |
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*/ |
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void increaseRegularization(); |
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/** |
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* @brief Decrease the state and control regularization values by a |
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* `regfactor_` factor |
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*/ |
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void decreaseRegularization(); |
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/** |
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* @brief Allocate all the internal data needed for the solver |
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*/ |
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virtual void allocateData(); |
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/** |
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* @brief Return the regularization factor used to increase the damping value |
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*/ |
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double get_reg_incfactor() const; |
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/** |
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* @brief Return the regularization factor used to decrease the damping value |
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*/ |
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double get_reg_decfactor() const; |
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/** |
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* @brief Return the regularization factor used to decrease / increase it |
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*/ |
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DEPRECATED("Use get_reg_incfactor() or get_reg_decfactor()", |
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double get_regfactor() const;) |
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/** |
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* @brief Return the minimum regularization value |
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*/ |
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double get_reg_min() const; |
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DEPRECATED("Use get_reg_min()", double get_regmin() const); |
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/** |
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* @brief Return the maximum regularization value |
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*/ |
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double get_reg_max() const; |
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DEPRECATED("Use get_reg_max()", double get_regmax() const); |
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/** |
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* @brief Return the set of step lengths using by the line-search procedure |
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*/ |
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const std::vector<double>& get_alphas() const; |
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/** |
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* @brief Return the step-length threshold used to decrease regularization |
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*/ |
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double get_th_stepdec() const; |
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/** |
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* @brief Return the step-length threshold used to increase regularization |
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*/ |
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double get_th_stepinc() const; |
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/** |
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* @brief Return the tolerance of the expected gradient used for testing the |
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* step |
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*/ |
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double get_th_grad() const; |
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/** |
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* @brief Return the Hessian of the Value function \f$V_{\mathbf{xx}_s}\f$ |
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*/ |
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const std::vector<Eigen::MatrixXd>& get_Vxx() const; |
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/** |
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* @brief Return the Hessian of the Value function \f$V_{\mathbf{x}_s}\f$ |
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*/ |
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const std::vector<Eigen::VectorXd>& get_Vx() const; |
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/** |
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* @brief Return the Hessian of the Hamiltonian function |
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* \f$\mathbf{Q}_{\mathbf{xx}_s}\f$ |
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*/ |
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const std::vector<Eigen::MatrixXd>& get_Qxx() const; |
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/** |
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* @brief Return the Hessian of the Hamiltonian function |
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* \f$\mathbf{Q}_{\mathbf{xu}_s}\f$ |
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*/ |
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const std::vector<Eigen::MatrixXd>& get_Qxu() const; |
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/** |
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* @brief Return the Hessian of the Hamiltonian function |
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* \f$\mathbf{Q}_{\mathbf{uu}_s}\f$ |
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*/ |
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const std::vector<Eigen::MatrixXd>& get_Quu() const; |
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/** |
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* @brief Return the Jacobian of the Hamiltonian function |
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* \f$\mathbf{Q}_{\mathbf{x}_s}\f$ |
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*/ |
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const std::vector<Eigen::VectorXd>& get_Qx() const; |
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/** |
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* @brief Return the Jacobian of the Hamiltonian function |
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* \f$\mathbf{Q}_{\mathbf{u}_s}\f$ |
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*/ |
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const std::vector<Eigen::VectorXd>& get_Qu() const; |
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/** |
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* @brief Return the feedback gains \f$\mathbf{K}_{s}\f$ |
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*/ |
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const std::vector<MatrixXdRowMajor>& get_K() const; |
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/** |
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* @brief Return the feedforward gains \f$\mathbf{k}_{s}\f$ |
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*/ |
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const std::vector<Eigen::VectorXd>& get_k() const; |
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/** |
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* @brief Modify the regularization factor used to increase the damping value |
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*/ |
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void set_reg_incfactor(const double reg_factor); |
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/** |
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* @brief Modify the regularization factor used to decrease the damping value |
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*/ |
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void set_reg_decfactor(const double reg_factor); |
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/** |
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* @brief Modify the regularization factor used to decrease / increase it |
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*/ |
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DEPRECATED("Use set_reg_incfactor() or set_reg_decfactor()", |
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void set_regfactor(const double reg_factor);) |
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/** |
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* @brief Modify the minimum regularization value |
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*/ |
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void set_reg_min(const double regmin); |
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DEPRECATED("Use set_reg_min()", void set_regmin(const double regmin)); |
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/** |
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* @brief Modify the maximum regularization value |
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*/ |
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void set_reg_max(const double regmax); |
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DEPRECATED("Use set_reg_max()", void set_regmax(const double regmax)); |
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/** |
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* @brief Modify the set of step lengths using by the line-search procedure |
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*/ |
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void set_alphas(const std::vector<double>& alphas); |
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/** |
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* @brief Modify the step-length threshold used to decrease regularization |
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*/ |
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void set_th_stepdec(const double th_step); |
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/** |
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* @brief Modify the step-length threshold used to increase regularization |
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*/ |
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void set_th_stepinc(const double th_step); |
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/** |
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* @brief Modify the tolerance of the expected gradient used for testing the |
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* step |
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*/ |
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void set_th_grad(const double th_grad); |
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protected: |
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double reg_incfactor_; //!< Regularization factor used to increase the |
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//!< damping value |
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double reg_decfactor_; //!< Regularization factor used to decrease the |
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//!< damping value |
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double reg_min_; //!< Minimum allowed regularization value |
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double reg_max_; //!< Maximum allowed regularization value |
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double cost_try_; //!< Total cost computed by line-search procedure |
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std::vector<Eigen::VectorXd> |
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xs_try_; //!< State trajectory computed by line-search procedure |
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std::vector<Eigen::VectorXd> |
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us_try_; //!< Control trajectory computed by line-search procedure |
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std::vector<Eigen::VectorXd> |
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dx_; //!< State error during the roll-out/forward-pass (size T) |
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// allocate data |
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std::vector<Eigen::MatrixXd> |
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Vxx_; //!< Hessian of the Value function \f$\mathbf{V_{xx}}\f$ |
365 |
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Eigen::MatrixXd |
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Vxx_tmp_; //!< Temporary variable for ensuring symmetry of Vxx |
367 |
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std::vector<Eigen::VectorXd> |
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Vx_; //!< Gradient of the Value function \f$\mathbf{V_x}\f$ |
369 |
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std::vector<Eigen::MatrixXd> |
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Qxx_; //!< Hessian of the Hamiltonian \f$\mathbf{Q_{xx}}\f$ |
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std::vector<Eigen::MatrixXd> |
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Qxu_; //!< Hessian of the Hamiltonian \f$\mathbf{Q_{xu}}\f$ |
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std::vector<Eigen::MatrixXd> |
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Quu_; //!< Hessian of the Hamiltonian \f$\mathbf{Q_{uu}}\f$ |
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std::vector<Eigen::VectorXd> |
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Qx_; //!< Gradient of the Hamiltonian \f$\mathbf{Q_x}\f$ |
377 |
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std::vector<Eigen::VectorXd> |
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Qu_; //!< Gradient of the Hamiltonian \f$\mathbf{Q_u}\f$ |
379 |
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std::vector<MatrixXdRowMajor> K_; //!< Feedback gains \f$\mathbf{K}\f$ |
380 |
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std::vector<Eigen::VectorXd> k_; //!< Feed-forward terms \f$\mathbf{l}\f$ |
381 |
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|
382 |
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Eigen::VectorXd xnext_; //!< Next state \f$\mathbf{x}^{'}\f$ |
383 |
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MatrixXdRowMajor FxTVxx_p_; //!< Store the value of |
384 |
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//!< \f$\mathbf{f_x}^T\mathbf{V_{xx}}^{'}\f$ |
385 |
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std::vector<MatrixXdRowMajor> |
386 |
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FuTVxx_p_; //!< Store the values of |
387 |
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//!< \f$\mathbf{f_u}^T\mathbf{V_{xx}}^{'}\f$ |
388 |
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//!< per each running node |
389 |
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Eigen::VectorXd fTVxx_p_; //!< Store the value of |
390 |
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//!< \f$\mathbf{\bar{f}}^T\mathbf{V_{xx}}^{'}\f$ |
391 |
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std::vector<Eigen::LLT<Eigen::MatrixXd> > Quu_llt_; //!< Cholesky LLT solver |
392 |
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std::vector<Eigen::VectorXd> |
393 |
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Quuk_; //!< Store the values of \f$\mathbf{Q_{uu}\mathbf{k}} per each |
394 |
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//!< running node |
395 |
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std::vector<double> |
396 |
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alphas_; //!< Set of step lengths using by the line-search procedure |
397 |
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double th_grad_; //!< Tolerance of the expected gradient used for testing the |
398 |
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//!< step |
399 |
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double |
400 |
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th_stepdec_; //!< Step-length threshold used to decrease regularization |
401 |
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double |
402 |
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th_stepinc_; //!< Step-length threshold used to increase regularization |
403 |
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}; |
404 |
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405 |
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} // namespace crocoddyl |
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407 |
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#endif // CROCODDYL_CORE_SOLVERS_DDP_HPP_ |