Class ComposableTask

Inheritance Relationships

Base Type

  • public stomp::Task

Class Documentation

class ComposableTask : public stomp::Task

Public Functions

inline ComposableTask(NoiseGeneratorFn noise_generator_fn, CostFn cost_fn, FilterFn filter_fn, PostIterationFn post_iteration_fn, DoneFn done_fn)
~ComposableTask() = default
inline bool generateNoisyParameters(const Eigen::MatrixXd &parameters, std::size_t, std::size_t, int, int, Eigen::MatrixXd &parameters_noise, Eigen::MatrixXd &noise) override

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

inline bool computeCosts(const Eigen::MatrixXd &parameters, std::size_t, std::size_t, int, Eigen::VectorXd &costs, bool &validity) override

computes the state costs as a function of the distance from the bias parameters

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

inline bool computeNoisyCosts(const Eigen::MatrixXd &parameters, std::size_t, std::size_t, int, int, Eigen::VectorXd &costs, bool &validity) override

computes the state costs as a function of the distance from the bias parameters

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.

  • 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

inline bool filterParameterUpdates(std::size_t, std::size_t, int, const Eigen::MatrixXd &parameters, Eigen::MatrixXd &updates) override

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

inline void postIteration(std::size_t, std::size_t, int iteration_number, double cost, const Eigen::MatrixXd &parameters) override

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].

inline void done(bool success, int total_iterations, double final_cost, const Eigen::MatrixXd &parameters) override

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]