Inheritance diagram for stomp_motion_planner::CovariantTrajectoryPolicy:

Public Member Functions
bool	initialize ()

bool	initialize (const int num_time_steps, const int num_dimensions, const double movement_duration, const double cost_ridge_factor, const std::vector< double > &derivative_costs)

bool	setToMinControlCost (Eigen::VectorXd &start, Eigen::VectorXd &goal)
	compute the minmal control costs given a start and goal state

bool	getParametersAll (std::vector< Eigen::VectorXd > &parameters)

void	setFileNameBase (const std::string &file_name_base)

void	setPrintDebug (bool print_debug)

bool	setNumTimeSteps (const int num_time_steps)
	Sets the number of time steps used in reinforcement learning. More...

bool	getNumTimeSteps (int &num_time_steps)
	Gets the number of time steps used in reinforcement learning. More...

bool	getNumDimensions (int &num_dimensions)
	Gets the number of dimensions. More...

bool	getNumParameters (std::vector< int > &num_params)
	Gets the number of policy parameters per dimension. More...

bool	getBasisFunctions (std::vector< Eigen::MatrixXd > &basis_functions)
	Gets the basis functions that multiply the policy parameters in the dynamical system. More...

bool	getControlCosts (std::vector< Eigen::MatrixXd > &control_costs)
	Gets the positive semi-definite matrix of the quadratic control cost The weight of this control cost is provided by the task. More...

bool	updateParameters (const std::vector< Eigen::MatrixXd > &updates)
	Update the policy parameters based on the updates per timestep. More...

void	printParameters ()
	print paramters in standard output

bool	getParameters (std::vector< Eigen::VectorXd > &parameters)
	Get the policy parameters per dimension. More...

bool	setParameters (const std::vector< Eigen::VectorXd > &parameters)
	Set the policy parameters per dimension. More...

bool	computeControlCosts (const std::vector< Eigen::MatrixXd > &control_cost_matrices, const std::vector< std::vector< Eigen::VectorXd > > &parameters, const double weight, std::vector< Eigen::VectorXd > &control_costs)
	Compute the control costs over time, given the control cost matrix per dimension and parameters over time. More...

bool	computeControlCosts (const std::vector< Eigen::MatrixXd > &control_cost_matrices, const std::vector< Eigen::VectorXd > &parameters, const std::vector< Eigen::VectorXd > &noise, const double weight, std::vector< Eigen::VectorXd > &control_costs)

std::string	getInfoString ()

std::string	getClassName ()

bool	readFromDisc (const std::string directory_name, const int item_id, const int trial_id=0)

bool	writeToDisc (const int trial_id=0)

bool	readFromDisc (const std::string abs_file_name)

bool	writeToDisc (const std::string abs_file_name)

std::string	getFileName (const int trial_id)

Public Attributes
double	movement_dt_

Member Function Documentation

bool stomp_motion_planner::CovariantTrajectoryPolicy::computeControlCosts	(	const std::vector< Eigen::MatrixXd > &	control_cost_matrices,
		const std::vector< std::vector< Eigen::VectorXd > > &	parameters,
		const double	weight,
		std::vector< Eigen::VectorXd > &	control_costs
	)

virtual

Compute the control costs over time, given the control cost matrix per dimension and parameters over time.

Parameters

control_cost_matrices	(input) [num_dimensions] num_parameters x num_parameters: Quadratic control cost matrix (R)
parameters	(input) [num_dimensions][num_time_steps] num_parameters: Parameters over time (can also be theta + projected noise)
weight	(input) constant multiplier for the control costs
control_costs	(output) [num_dimensions] num_time_steps: Control costs over time

Returns

Implements stomp_motion_planner::Policy.

bool stomp_motion_planner::CovariantTrajectoryPolicy::getBasisFunctions ( std::vector< Eigen::MatrixXd > & basis_functions )

inlinevirtual

Gets the basis functions that multiply the policy parameters in the dynamical system.

Parameters

basis_function_matrix_array (output) Array of "num_time_steps x num_parameters" matrices, per dimension

Returns: true on success, false on failure

Implements stomp_motion_planner::Policy.

bool stomp_motion_planner::CovariantTrajectoryPolicy::getControlCosts ( std::vector< Eigen::MatrixXd > & control_costs )

inlinevirtual

Gets the positive semi-definite matrix of the quadratic control cost The weight of this control cost is provided by the task.

Parameters

control_cost_matrix (output) Array of square, positive semi-definite matrix: num_params x num_params

Returns: true on success, false on failure

Implements stomp_motion_planner::Policy.

bool stomp_motion_planner::CovariantTrajectoryPolicy::getNumDimensions ( int & num_dimensions )

inlinevirtual

Gets the number of dimensions.

Parameters

num_dimensions (output) number of dimensions

Returns: true on success, false on failure

Implements stomp_motion_planner::Policy.

bool stomp_motion_planner::CovariantTrajectoryPolicy::getNumParameters ( std::vector< int > & num_params )

inlinevirtual

Gets the number of policy parameters per dimension.

Parameters

num_params (output) vector of number of parameters per dimension

Returns: true on success, false on failure

Implements stomp_motion_planner::Policy.

bool stomp_motion_planner::CovariantTrajectoryPolicy::getNumTimeSteps ( int & num_time_steps )

inlinevirtual

Gets the number of time steps used in reinforcement learning.

Parameters

num_time_steps

Returns: true on success, fase on failure

Implements stomp_motion_planner::Policy.

bool stomp_motion_planner::CovariantTrajectoryPolicy::getParameters ( std::vector< Eigen::VectorXd > & parameters )

inlinevirtual

Get the policy parameters per dimension.

Parameters

parameters (output) array of parameter vectors

Returns: true on success, false on failure

Implements stomp_motion_planner::Policy.

bool stomp_motion_planner::CovariantTrajectoryPolicy::setNumTimeSteps ( const int num_time_steps )

inlinevirtual

Sets the number of time steps used in reinforcement learning.

Parameters

num_time_steps

Returns: true on success, false on failure

Implements stomp_motion_planner::Policy.

bool stomp_motion_planner::CovariantTrajectoryPolicy::setParameters ( const std::vector< Eigen::VectorXd > & parameters )

inlinevirtual

Set the policy parameters per dimension.

Parameters

parameters (input) array of parameter vectors

Returns: true on success, false on failure

Implements stomp_motion_planner::Policy.

bool stomp_motion_planner::CovariantTrajectoryPolicy::updateParameters ( const std::vector< Eigen::MatrixXd > & updates )

virtual

Update the policy parameters based on the updates per timestep.

Parameters

updates (input) parameter updates per time-step, num_time_steps x num_parameters

Returns: true on success, false on failure

Implements stomp_motion_planner::Policy.

The documentation for this class was generated from the following files:

src/planner/TrajectoryOptim/Stomp/covariant_trajectory_policy.hpp
src/planner/TrajectoryOptim/Stomp/covariant_trajectory_policy.cpp

Public Member Functions

Public Attributes

Member Function Documentation