Defines the STOMP improvement policy. More...

#include <task.h>

Inheritance diagram for stomp_core::Task:

Public Member Functions
virtual bool	computeCosts (const Eigen::MatrixXd &parameters, std::size_t start_timestep, std::size_t num_timesteps, int iteration_number, Eigen::VectorXd &costs, bool &validity)=0
	computes the state costs as a function of the optimized parameters for each time step.
virtual bool	computeNoisyCosts (const Eigen::MatrixXd &parameters, std::size_t start_timestep, std::size_t num_timesteps, int iteration_number, int rollout_number, Eigen::VectorXd &costs, bool &validity)=0
	computes the state costs as a function of the noisy parameters for each time step.
virtual void	done (bool success, int total_iterations, double final_cost, const Eigen::MatrixXd &parameters)
	Called by Stomp at the end of the optimization process.
virtual bool	filterNoisyParameters (std::size_t start_timestep, std::size_t num_timesteps, int iteration_number, int rollout_number, Eigen::MatrixXd &parameters, bool &filtered)
	Filters the given noisy parameters which is applied after noisy trajectory generation. It could be used for clipping of joint limits or projecting into the null space of the Jacobian.
virtual bool	filterParameterUpdates (std::size_t start_timestep, std::size_t num_timesteps, int iteration_number, const Eigen::MatrixXd &parameters, Eigen::MatrixXd &updates)
	Filters the given parameters which is applied after the update. It could be used for clipping of joint limits or projecting into the null space of the Jacobian.
virtual bool	generateNoisyParameters (const Eigen::MatrixXd &parameters, std::size_t start_timestep, std::size_t num_timesteps, int iteration_number, int rollout_number, Eigen::MatrixXd &parameters_noise, Eigen::MatrixXd &noise)=0
	Generates a noisy trajectory from the parameters.
virtual void	postIteration (std::size_t start_timestep, std::size_t num_timesteps, int iteration_number, double cost, const Eigen::MatrixXd &parameters)
	Called by STOMP at the end of each iteration.

Detailed Description

Defines the STOMP improvement policy.

Definition at line 41 of file task.h.

Member Function Documentation

virtual bool stomp_core::Task::computeCosts	(	const Eigen::MatrixXd &	parameters,
		std::size_t	start_timestep,
		std::size_t	num_timesteps,
		int	iteration_number,
		Eigen::VectorXd &	costs,
		bool &	validity
	)		`[pure virtual]`

computes the state costs as a function of the optimized parameters for each time step.

Parameters:

parameters	A matrix [num_dimensions][num_parameters] of the policy parameters to execute
start_timestep	The start index into the 'parameters' array, usually 0.
num_timesteps	The number of elements to use from 'parameters' starting from 'start_timestep'
iteration_number	The current iteration count in the optimization loop
costs	A vector containing the state costs per timestep.
validity	Whether or not the trajectory is valid

Returns:: True if cost were properly computed, otherwise false

Implemented in stomp_core_examples::SimpleOptimizationTask.

virtual bool stomp_core::Task::computeNoisyCosts	(	const Eigen::MatrixXd &	parameters,
		std::size_t	start_timestep,
		std::size_t	num_timesteps,
		int	iteration_number,
		int	rollout_number,
		Eigen::VectorXd &	costs,
		bool &	validity
	)		`[pure virtual]`

computes the state costs as a function of the noisy parameters for each time step.

Parameters:

parameters	A matrix [num_dimensions][num_parameters] of the policy parameters to execute
start_timestep	The start index into the 'parameters' array, usually 0.
num_timesteps	The number of elements to use from 'parameters' starting from 'start_timestep'
iteration_number	The current iteration count in the optimization loop
rollout_number	The index of the noisy trajectory whose cost is being evaluated.
costs	vector A vector containing the state costs per timestep.
validity	Whether or not the trajectory is valid

Returns:: True if cost were properly computed, otherwise false

Implemented in stomp_core_examples::SimpleOptimizationTask.

virtual void stomp_core::Task::done	(	bool	success,
		int	total_iterations,
		double	final_cost,
		const Eigen::MatrixXd &	parameters
	)		`[inline, virtual]`

Called by Stomp at the end of the optimization process.

Parameters:

success	Whether the optimization succeeded
total_iterations	Number of iterations used
final_cost	The cost value after optimizing.
parameters	The parameters generated at the end of the optimization [num_dimensions x num_timesteps]

Definition at line 166 of file task.h.

virtual bool stomp_core::Task::filterNoisyParameters	(	std::size_t	start_timestep,
		std::size_t	num_timesteps,
		int	iteration_number,
		int	rollout_number,
		Eigen::MatrixXd &	parameters,
		bool &	filtered
	)		`[inline, virtual]`

Filters the given noisy parameters which is applied after noisy trajectory generation. It could be used for clipping of joint limits or projecting into the null space of the Jacobian.

Parameters:

start_timestep	The start index into the 'parameters' array, usually 0.
num_timesteps	The number of elements to use from 'parameters' starting from 'start_timestep'
iteration_number	The current iteration count in the optimization loop
rollout_number	The rollout index for this noisy parameter set
parameters	The noisy parameters *
filtered	False if no filtering was done

Returns:: False if no filtering was done, otherwise true

Definition at line 115 of file task.h.

virtual bool stomp_core::Task::filterParameterUpdates	(	std::size_t	start_timestep,
		std::size_t	num_timesteps,
		int	iteration_number,
		const Eigen::MatrixXd &	parameters,
		Eigen::MatrixXd &	updates
	)		`[inline, virtual]`

Filters the given parameters which is applied after the update. It could be used for clipping of joint limits or projecting into the null space of the Jacobian.

Parameters:

start_timestep	The start index into the 'parameters' array, usually 0.
num_timesteps	The number of elements to use from 'parameters' starting from 'start_timestep'
iteration_number	The current iteration count in the optimization loop
parameters	The optimized parameters
updates	The updates to the parameters

Returns:: True if successful, otherwise false

Reimplemented in stomp_core_examples::SimpleOptimizationTask, and DummyTask.

Definition at line 137 of file task.h.

virtual bool stomp_core::Task::generateNoisyParameters	(	const Eigen::MatrixXd &	parameters,
		std::size_t	start_timestep,
		std::size_t	num_timesteps,
		int	iteration_number,
		int	rollout_number,
		Eigen::MatrixXd &	parameters_noise,
		Eigen::MatrixXd &	noise
	)		`[pure virtual]`

Generates a noisy trajectory from the parameters.

Parameters:

parameters	A matrix [num_dimensions][num_parameters] of the current optimized parameters
start_timestep	The start index into the 'parameters' array, usually 0.
num_timesteps	The number of elements to use from 'parameters' starting from 'start_timestep'
iteration_number	The current iteration count in the optimization loop
rollout_number	The index of the noisy trajectory.
parameters_noise	The parameters + noise
noise	The noise applied to the parameters

Returns:: True if cost were properly computed, otherwise false

Implemented in stomp_core_examples::SimpleOptimizationTask, and DummyTask.

virtual void stomp_core::Task::postIteration	(	std::size_t	start_timestep,
		std::size_t	num_timesteps,
		int	iteration_number,
		double	cost,
		const Eigen::MatrixXd &	parameters
	)		`[inline, virtual]`

Called by STOMP at the end of each iteration.

Parameters:

start_timestep	The start index into the 'parameters' array, usually 0.
num_timesteps	The number of elements to use from 'parameters' starting from 'start_timestep'
iteration_number	The current iteration count in the optimization loop
cost	The cost value for the current parameters.
parameters	The value of the parameters at the end of the current iteration [num_dimensions x num_timesteps].

Definition at line 154 of file task.h.

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

task.h

Public Member Functions

Detailed Description

Member Function Documentation