Implementation of Contextual Dynamical Movement Primitives. More...

#include <DmpContextualOneStep.hpp>

Inheritance diagram for DmpContextualOneStep:

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Public Member Functions
	DmpContextualOneStep (int n_dims_dmp, std::vector< FunctionApproximator * > function_approximators, DmpType dmp_type=KULVICIUS_2012_JOINING)
	Initialization constructor for Contextual DMPs of known dimensionality, but with unknown initial and attractor states. More...

void	computeFunctionApproximatorOutput (const Eigen::Ref< const Eigen::MatrixXd > &phase_state, Eigen::MatrixXd &fa_output) const
	Compute the outputs of the function approximators. More...

void	train (const std::vector< Trajectory > &trajectories, const std::vector< Eigen::MatrixXd > &task_parameters, std::string save_directory="", bool overwrite=false)
	Train a contextual Dmp with a set of trajectories (and save results to file) This function is useful for debugging, i.e. More...

Public Member Functions inherited from DmpContextual
	DmpContextual (int n_dims_dmp, std::vector< FunctionApproximator * > function_approximators, DmpType dmp_type)
	Initialization constructor for Contextual DMPs of known dimensionality, but with unknown initial and attractor states. More...

void	set_task_parameters (const Eigen::MatrixXd &task_parameters)
	Set the current task parameters. More...

void	set_policy_parameter_function_goal (FunctionApproximator *function_approximator)
	Set a function approximator to predict the goal from the task parameters. More...

void	set_policy_parameter_function_duration (FunctionApproximator *function_approximator)
	Set a function approximator to predict the duration from the task parameters. More...

void	train (const std::vector< Trajectory > &trajectories, const std::vector< Eigen::MatrixXd > &task_parameters, std::string save_directory)
	Train a contextual DMP with multiple trajectories. More...

void	train (const std::vector< Trajectory > &trajectories, const std::vector< Eigen::MatrixXd > &task_parameters)
	Train a contextual DMP with multiple trajectories. More...

void	train (const std::vector< Trajectory > &trajectories, std::string save_directory, bool overwrite)
	Train a contextual DMP with multiple trajectories. More...

void	train (const std::vector< Trajectory > &trajectories, std::string save_directory)
	Train a contextual DMP with multiple trajectories. More...

void	train (const std::vector< Trajectory > &trajectories)
	Train a contextual DMP with multiple trajectories. More...

Public Member Functions inherited from Dmp
	Dmp (double tau, Eigen::VectorXd y_init, Eigen::VectorXd y_attr, std::vector< FunctionApproximator * > function_approximators, double alpha_spring_damper, DynamicalSystem goal_system, DynamicalSystem phase_system, DynamicalSystem *gating_system, ForcingTermScaling scaling=NO_SCALING)
	Initialization constructor. More...

	Dmp (int n_dims_dmp, std::vector< FunctionApproximator * > function_approximators, double alpha_spring_damper, DynamicalSystem goal_system, DynamicalSystem phase_system, DynamicalSystem *gating_system, ForcingTermScaling scaling=NO_SCALING)
	Initialization constructor for Dmps of known dimensionality, but with unknown initial and attractor states. More...

	Dmp (double tau, Eigen::VectorXd y_init, Eigen::VectorXd y_attr, std::vector< FunctionApproximator * > function_approximators, DmpType dmp_type=KULVICIUS_2012_JOINING, ForcingTermScaling scaling=NO_SCALING)
	Constructor that initializes the DMP with default dynamical systems. More...

	Dmp (int n_dims_dmp, std::vector< FunctionApproximator * > function_approximators, DmpType dmp_type=KULVICIUS_2012_JOINING, ForcingTermScaling scaling=NO_SCALING)
	Initialization constructor for Dmps of known dimensionality, but with unknown initial and attractor states. More...

	Dmp (double tau, Eigen::VectorXd y_init, Eigen::VectorXd y_attr, double alpha_spring_damper, DynamicalSystem *goal_system)
	Initialization constructor for Dmps without a forcing term. More...

	~Dmp (void)
	Destructor. More...

Dmp *	clone (void) const
	Return a deep copy of this object. More...

virtual void	integrateStart (Eigen::Ref< Eigen::VectorXd > x, Eigen::Ref< Eigen::VectorXd > xd) const
	Start integrating the system. More...

void	differentialEquation (const Eigen::Ref< const Eigen::VectorXd > &x, Eigen::Ref< Eigen::VectorXd > xd) const
	The differential equation which defines the system. More...

void	analyticalSolution (const Eigen::VectorXd &ts, Eigen::MatrixXd &xs, Eigen::MatrixXd &xds, Eigen::MatrixXd &forcing_terms, Eigen::MatrixXd &fa_output) const
	Return analytical solution of the system at certain times (and return forcing terms) More...

void	analyticalSolution (const Eigen::VectorXd &ts, Eigen::MatrixXd &xs, Eigen::MatrixXd &xds, Eigen::MatrixXd &forcing_terms) const
	Return analytical solution of the system at certain times (and return forcing terms) More...

void	analyticalSolution (const Eigen::VectorXd &ts, Eigen::MatrixXd &xs, Eigen::MatrixXd &xds) const
	Return analytical solution of the system at certain times. More...

virtual void	analyticalSolution (const Eigen::VectorXd &ts, Trajectory &trajectory) const
	Return analytical solution of the system at certain times. More...

void	analyticalSolution (const Eigen::VectorXd &ts, Trajectory &trajectory, Eigen::MatrixXd &forcing_terms) const
	Return analytical solution of the system at certain times. More...

virtual void	statesAsTrajectory (const Eigen::MatrixXd &x_in, const Eigen::MatrixXd &xd_in, Eigen::MatrixXd &y_out, Eigen::MatrixXd &yd_out, Eigen::MatrixXd &ydd_out) const
	Get the output of a DMP dynamical system as a trajectory. More...

virtual void	statesAsTrajectory (const Eigen::VectorXd &ts, const Eigen::MatrixXd &x_in, const Eigen::MatrixXd &xd_in, Trajectory &trajectory) const
	Get the output of a DMP dynamical system as a trajectory. More...

virtual void	train (const Trajectory &trajectory)
	Train a DMP with a trajectory. More...

virtual void	train (const Trajectory &trajectory, std::string save_directory, bool overwrite=false)
	Train a DMP with a trajectory, and write results to file. More...

virtual void	set_tau (double tau)
	Accessor function for the time constant. More...

virtual void	set_initial_state (const Eigen::VectorXd &y_init)
	Accessor function for the initial state of the system. More...

virtual void	set_attractor_state (const Eigen::VectorXd &y_attr)
	Accessor function for the attractor state of the system. More...

void	set_damping_coefficient (double damping_coefficient)
	Accessor function for damping coefficient of spring-damper system. More...

void	set_spring_constant (double spring_constant)
	Accessor function for spring constant of spring-damper system. More...

std::string	toString (void) const
	Returns a string representation of the object. More...

void	getSelectableParameters (std::set< std::string > &selectable_values_labels) const
	Return all the names of the parameter types that can be selected. More...

void	setSelectedParameters (const std::set< std::string > &selected_values_labels)
	Determine which subset of parameters is represented in the vector returned by Parameterizable::getParameterVectorSelected. More...

int	getParameterVectorAllSize (void) const
	Get the size of the parameter values vector when it contains all available parameter values. More...

void	getParameterVectorAll (Eigen::VectorXd &values) const
	Return a vector that returns all available parameter values. More...

void	setParameterVectorAll (const Eigen::VectorXd &values)
	Set all available parameter values with one vector. More...

void	getParameterVectorMask (const std::set< std::string > selected_values_labels, Eigen::VectorXi &selected_mask) const
	Get a mask for selecting parameters. More...

void	computeFunctionApproximatorInputsAndTargets (const Trajectory &trajectory, Eigen::VectorXd &fa_inputs_phase, Eigen::MatrixXd &fa_targets) const
	Given a trajectory, compute the inputs and targets for the function approximators. More...

void	set_perturbation_analytical_solution (double perturbation_standard_deviation)
	Add a perturbation to the forcing term when computing the analytical solution. More...

double	get_perturbation_analytical_solution () const
	Get the perturbation to the forcing term when computing the analytical solution. More...

Public Member Functions inherited from DynamicalSystem
	DynamicalSystem (int order, double tau, Eigen::VectorXd initial_state, Eigen::VectorXd attractor_state, std::string name)
	Initialization constructor. More...

virtual	~DynamicalSystem (void)
	Destructor.

void	integrateStart (const Eigen::VectorXd &x_init, Eigen::Ref< Eigen::VectorXd > x, Eigen::Ref< Eigen::VectorXd > xd)
	Start integrating the system with a new initial state. More...

virtual void	integrateStep (double dt, const Eigen::Ref< const Eigen::VectorXd > x, Eigen::Ref< Eigen::VectorXd > x_updated, Eigen::Ref< Eigen::VectorXd > xd_updated) const
	Integrate the system one time step. More...

void	set_integration_method (IntegrationMethod integration_method)
	Choose the integration method. More...

int	dim (void) const
	Get the dimensionality of the dynamical system, i.e. More...

int	dim_orig (void) const
	Get the dimensionality of the dynamical system, i.e. More...

double	tau (void) const
	Accessor function for the time constant. More...

Eigen::VectorXd	initial_state (void) const
	Accessor function for the initial state of the dynamical system. More...

void	initial_state (Eigen::VectorXd &initial_state) const
	Accessor function for the initial state of the dynamical system. More...

Eigen::VectorXd	attractor_state (void) const
	Accessor function for the attractor state of the dynamical system. More...

void	attractor_state (Eigen::VectorXd &attractor_state) const
	Accessor function for the attractor state of the dynamical system. More...

virtual void	set_attractor_state (const Eigen::Ref< const Eigen::VectorXd > &attractor_state)
	Mutator function for the attractor state of the dynamical system. More...

std::string	name (void) const
	Accessor function for the name of the dynamical system. More...

virtual void	set_name (std::string name)
	Mutator function for the name of the dynamical system. More...

Public Member Functions inherited from Parameterizable
virtual	~Parameterizable (void)
	Destructor.

virtual int	getParameterVectorSelectedSize (void) const
	Get the size of the vector of selected parameters, as returned by getParameterVectorSelected(. More...

virtual void	getParameterVectorSelected (Eigen::VectorXd &values, bool normalized=false) const
	Get the values of the selected parameters in one vector. More...

virtual void	getParameterVectorSelectedNormalized (Eigen::VectorXd &values) const
	Get the normalized values of the selected parameters in one vector. More...

void	getParameterVectorSelectedMinMax (Eigen::VectorXd &min, Eigen::VectorXd &max) const
	Get the minimum and maximum of the selected parameters in one vector. More...

void	getParameterVectorAllMinMax (Eigen::VectorXd &min, Eigen::VectorXd &max) const
	Get the minimum and maximum values of the current parameter vector. More...

void	getParameterVectorSelectedRanges (Eigen::VectorXd &ranges) const
	Get the ranges of the selected parameters, i.e. More...

virtual void	setParameterVectorSelected (const Eigen::VectorXd &values, bool normalized=false)
	Set all the values of the selected parameters with one vector. More...

virtual void	setParameterVectorSelectedNormalized (const Eigen::VectorXd &values)
	Set all the values of the selected parameters with one vector of normalized values. More...

void	setSelectedParametersOne (std::string selected)
	Set the parameters that are currently selected. More...

void	setParameterVectorModifier (std::string modifier, bool new_value)
	Turn certain modifiers on or off. More...

void	setVectorLengthsPerDimension (const Eigen::VectorXi &lengths_per_dimension)
	The vector (VectorXd) with parameter values can be split into different parts (as vector<VectorXd>; this function specifices the length of each sub-vector. More...

Eigen::VectorXi	getVectorLengthsPerDimension (void) const
	Get the specified length of each vector in each dimension. More...

void	getParameterVectorSelected (std::vector< Eigen::VectorXd > &values, bool normalized=false) const
	Get the values of the selected parameters in one vector. More...

void	setParameterVectorSelected (const std::vector< Eigen::VectorXd > &values, bool normalized=false)
	Set all the values of the selected parameters with a vector of vectors. More...

Protected Member Functions
	DmpContextualOneStep (void)
	Default constructor. More...

Protected Member Functions inherited from DmpContextual
void	trainLocal (const std::vector< Trajectory > &trajectories, const std::vector< Eigen::MatrixXd > &task_parameters, std::string save_directory, bool overwrite)
	Train a contextual DMP. More...

void	checkTrainTrajectories (const std::vector< Trajectory > &trajectories)
	Check if several trajectories have the same duration and initial/final states. More...

Protected Member Functions inherited from Dmp
FunctionApproximator *	function_approximator (int i_dim) const
	Get a pointer to the function approximator for a certain dimension. More...

Protected Member Functions inherited from DynamicalSystem
	DynamicalSystem (void)
	Default constructor. More...

void	set_dim (int dim)
	Set the dimensionality of the dynamical system, i.e. More...

Friends
class	boost::serialization::access
	Give boost serialization access to private members. More...

Additional Inherited Members
Public Types inherited from Dmp
enum	DmpType { IJSPEERT_2002_MOVEMENT, KULVICIUS_2012_JOINING, COUNTDOWN_2013 }
	Different types of DMPs that can be initialized. More...

enum	ForcingTermScaling { NO_SCALING, G_MINUS_Y0_SCALING, AMPLITUDE_SCALING }
	Different ways to scale the forcing term. More...

Public Types inherited from DynamicalSystem
enum	IntegrationMethod { EULER, RUNGE_KUTTA }
	The possible integration methods that can be used. More...

Protected Attributes inherited from DmpContextual
Eigen::MatrixXd	task_parameters_
	The current task parameters.

std::vector< FunctionApproximator * >	policy_parameter_function_goal_
	FunctionApproximators that relate task parameters the goal of the DMP.

FunctionApproximator *	policy_parameter_function_duration_
	FunctionApproximator that relates task parameters the duration of the DMP.

Detailed Description

Implementation of Contextual Dynamical Movement Primitives.

Definition at line 39 of file DmpContextualOneStep.hpp.

Constructor & Destructor Documentation

DmpContextualOneStep	(	int	n_dims_dmp,
		std::vector< FunctionApproximator * >	function_approximators,
		DmpType	dmp_type = `KULVICIUS_2012_JOINING`
	)

Initialization constructor for Contextual DMPs of known dimensionality, but with unknown initial and attractor states.

Initializes the DMP with default dynamical systems.

Parameters

n_dims_dmp	Dimensionality of the DMP
function_approximators	Function approximators for the forcing term
dmp_type	The type of DMP, see Dmp::DmpType

Definition at line 38 of file DmpContextualOneStep.cpp.

 :  DmpContextual(n_dims_dmp, function_approximators, dmp_type)
 {
 }

DmpContextualOneStep ( void )

inlineprotected

Default constructor.

Remarks: This default constuctor is required for boost::serialization to work. See Boost serialization issues

Definition at line 65 of file DmpContextualOneStep.hpp.

65 {};

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Member Function Documentation

void computeFunctionApproximatorOutput	(	const Eigen::Ref< const Eigen::MatrixXd > &	phase_state,
		Eigen::MatrixXd &	fa_output
	)		const

virtual

Compute the outputs of the function approximators.

Parameters

[in]	phase_state	The phase states for which the outputs are computed.
[out]	fa_output	The outputs of the function approximators.

Implements DmpContextual.

Definition at line 45 of file DmpContextualOneStep.cpp.

 {
   int n_time_steps = phase_state.rows(); 
   fa_output.resize(n_time_steps,dim_orig());
   fa_output.fill(0.0);
   
   MatrixXd task_parameters = task_parameters_;
   if (task_parameters.rows()==1)
   { 
     task_parameters = task_parameters.row(0).replicate(n_time_steps,1).eval();
   }
   else if (task_parameters.cols()==1)
   {
     task_parameters = task_parameters.col(0).transpose().replicate(n_time_steps,1).eval();
   }
 
 
   
   assert(n_time_steps==task_parameters.rows());
   
   int n_task_parameters = task_parameters.cols();
   MatrixXd fa_input(n_time_steps,n_task_parameters+1);
   fa_input << phase_state, task_parameters;
   
   
   MatrixXd output(n_time_steps,1);
   for (int dd=0; dd<dim_orig(); dd++)
   {
     if (function_approximator(dd)!=NULL)
     {
       if (function_approximator(dd)->isTrained()) 
       {
         function_approximator(dd)->predict(fa_input,output);
         if (output.size()>0)
         {
           fa_output.col(dd) = output;
         }
       }
     }
   }
 }

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void train	(	const std::vector< Trajectory > &	trajectories,
		const std::vector< Eigen::MatrixXd > &	task_parameters,
		std::string	save_directory = `""`,
		bool	overwrite = `false`
	)

virtual

Train a contextual Dmp with a set of trajectories (and save results to file) This function is useful for debugging, i.e.

if you want to save intermediate results to a directory

Parameters

[in]	trajectories	The set of trajectories
[in]	task_parameters	The task parameters for each of the trajectories.
[in]	save_directory	Directory to which to save intermediate results.
[in]	overwrite	Overwrite existing files in the directory above Overloads Dmp::train

Implements DmpContextual.

Definition at line 88 of file DmpContextualOneStep.cpp.

 {
   // Check if inputs are of the right size.
   unsigned int n_demonstrations = trajectories.size();
   assert(n_demonstrations==task_parameters.size());
   
   // Then check if the trajectories have the same duration and initial/final state
   // Later on, if they are not the same, they should be learned also.
   checkTrainTrajectories(trajectories);
 
   // Set tau, initial_state and attractor_state from the trajectories 
   set_tau(trajectories[0].duration());
   set_initial_state(trajectories[0].initial_y());
   set_attractor_state(trajectories[0].final_y());
   
   MatrixXd all_fa_inputs(0,0);
   MatrixXd all_fa_targets(0,0);  
   int n_task_parameters = -1;
   int n_time_steps_total = 0;
 
   VectorXd cur_fa_input_phase;
   MatrixXd cur_fa_target;
   MatrixXd cur_task_parameters;
   for (unsigned int i_demo=0; i_demo<n_demonstrations; i_demo++)
   {
     cur_task_parameters = task_parameters[i_demo]; 
     if (i_demo==0)
     {
       n_task_parameters = cur_task_parameters.cols();
       
       // This is the first time task_parameters_ is set, because this is the first time we know 
       // n_task_parameters.
       // We set it so that set_task_parameters can check if task_parameters_.cols()==n_task_parameters
       task_parameters_ = MatrixXd::Zero(1,n_task_parameters);
     }
     else
     {
       assert(n_task_parameters==cur_task_parameters.cols());
     }
     
     int n_time_steps = trajectories[i_demo].length();
     n_time_steps_total += n_time_steps;
 
     // Make sure cur_task_parameters has n_time_steps. Copy if it doesn't.
     if (cur_task_parameters.rows()==1 && n_time_steps>1) {      
       MatrixXd cur_task_parameters_tmp = cur_task_parameters.replicate(n_time_steps,1);
       cur_task_parameters = cur_task_parameters_tmp;
     }
     assert(n_time_steps==cur_task_parameters.rows());
     
     computeFunctionApproximatorInputsAndTargets(trajectories[i_demo], cur_fa_input_phase, cur_fa_target);
     
     all_fa_inputs.conservativeResize(n_time_steps_total,1+n_task_parameters);
     
     all_fa_inputs.bottomLeftCorner(n_time_steps,1) = cur_fa_input_phase;
     all_fa_inputs.bottomRightCorner(n_time_steps,n_task_parameters) = cur_task_parameters;
 
     all_fa_targets.conservativeResize(n_time_steps_total,dim_orig());
     all_fa_targets.bottomRows(n_time_steps) = cur_fa_target;
     
   }
 
   // We have all inputs and targets. Now train the function approximator for each dimension.
   
   for (int dd=0; dd<dim_orig(); dd++)
   {
     // This is just boring stuff to figure out if and where to store the results of training
     string save_directory_dim;
     if (!save_directory.empty())
     {
       if (dim_orig()==1)
         save_directory_dim = save_directory;
       else
         save_directory_dim = save_directory + "/dim" + to_string(dd);
     }
     
     VectorXd fa_target = all_fa_targets.col(dd);
     function_approximator(dd)->train(all_fa_inputs,fa_target,save_directory_dim,overwrite);
   }
   
 }

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Friends And Related Function Documentation

friend class boost::serialization::access

friend

Give boost serialization access to private members.

Definition at line 65 of file DmpContextualOneStep.hpp.

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

Public Member Functions

Protected Member Functions

Friends

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Friends And Related Function Documentation