:py:mod:`rofunc.utils.robolab.kinematics.pytorch_kinematics_utils` ================================================================== .. py:module:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils :allowtitles: Module Contents --------------- Classes ~~~~~~~ .. list-table:: :class: autosummary longtable :align: left * - :py:obj:`IKSolution ` - .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.IKSolution :summary: * - :py:obj:`LineSearch ` - .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.LineSearch :summary: * - :py:obj:`BacktrackingLineSearch ` - .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.BacktrackingLineSearch :summary: * - :py:obj:`InverseKinematics ` - .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.InverseKinematics :summary: * - :py:obj:`PseudoInverseIK ` - * - :py:obj:`PseudoInverseIKWithSVD ` - Functions ~~~~~~~~~ .. list-table:: :class: autosummary longtable :align: left * - :py:obj:`build_chain_from_model ` - .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.build_chain_from_model :summary: * - :py:obj:`gaussian_around_config ` - .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.gaussian_around_config :summary: * - :py:obj:`delta_pose ` - .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.delta_pose :summary: * - :py:obj:`apply_mask ` - .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.apply_mask :summary: Data ~~~~ .. list-table:: :class: autosummary longtable :align: left * - :py:obj:`JOINT_TYPE_MAP ` - .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.JOINT_TYPE_MAP :summary: API ~~~ .. py:data:: JOINT_TYPE_MAP :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.JOINT_TYPE_MAP :value: None .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.JOINT_TYPE_MAP .. py:function:: build_chain_from_model(model_path: str, verbose=False) :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.build_chain_from_model .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.build_chain_from_model .. py:class:: IKSolution(dof, num_problems, num_retries, pos_tolerance, rot_tolerance, device='cpu') :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.IKSolution .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.IKSolution .. rubric:: Initialization .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.IKSolution.__init__ .. py:method:: update_remaining_with_keep_mask(keep: torch.tensor) :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.IKSolution.update_remaining_with_keep_mask .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.IKSolution.update_remaining_with_keep_mask .. py:method:: update(q: torch.tensor, err: torch.tensor, use_keep_mask=True, keep_mask=None) :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.IKSolution.update .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.IKSolution.update .. py:function:: gaussian_around_config(config: torch.Tensor, std: float) -> typing.Callable[[int], torch.Tensor] :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.gaussian_around_config .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.gaussian_around_config .. py:class:: LineSearch :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.LineSearch .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.LineSearch .. py:method:: do_line_search(chain, q, dq, target_pos, target_wxyz, initial_dx, problem_remaining=None) :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.LineSearch.do_line_search :abstractmethod: .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.LineSearch.do_line_search .. py:class:: BacktrackingLineSearch(max_lr=1.0, decrease_factor=0.5, max_iterations=5, sufficient_decrease=0.01) :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.BacktrackingLineSearch Bases: :py:obj:`rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.LineSearch` .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.BacktrackingLineSearch .. rubric:: Initialization .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.BacktrackingLineSearch.__init__ .. py:method:: do_line_search(chain, q, dq, target_pos, target_wxyz, initial_dx, problem_remaining=None) :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.BacktrackingLineSearch.do_line_search .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.BacktrackingLineSearch.do_line_search .. py:class:: InverseKinematics(serial_chain: pytorch_kinematics.chain.SerialChain, pos_tolerance: float = 0.001, rot_tolerance: float = 0.01, retry_configs: typing.Optional[torch.Tensor] = None, num_retries: typing.Optional[int] = None, joint_limits: typing.Optional[torch.Tensor] = None, config_sampling_method: typing.Union[str, typing.Callable[[int], torch.Tensor]] = 'uniform', max_iterations: int = 50, lr: float = 0.2, line_search: typing.Optional[rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.LineSearch] = None, regularlization: float = 1e-09, debug=False, early_stopping_any_converged=False, early_stopping_no_improvement='any', early_stopping_no_improvement_patience=2, optimizer_method: typing.Union[str, typing.Type[torch.optim.Optimizer]] = 'sgd') :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.InverseKinematics .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.InverseKinematics .. rubric:: Initialization .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.InverseKinematics.__init__ .. py:method:: clear() :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.InverseKinematics.clear .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.InverseKinematics.clear .. py:method:: sample_configs(num_configs: int) -> torch.Tensor :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.InverseKinematics.sample_configs .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.InverseKinematics.sample_configs .. py:method:: solve(target_poses: pytorch_kinematics.transforms.Transform3d) -> rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.IKSolution :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.InverseKinematics.solve :abstractmethod: .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.InverseKinematics.solve .. py:function:: delta_pose(m: torch.tensor, target_pos, target_wxyz) :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.delta_pose .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.delta_pose .. py:function:: apply_mask(mask, *args) :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.apply_mask .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.apply_mask .. py:class:: PseudoInverseIK(serial_chain: pytorch_kinematics.chain.SerialChain, pos_tolerance: float = 0.001, rot_tolerance: float = 0.01, retry_configs: typing.Optional[torch.Tensor] = None, num_retries: typing.Optional[int] = None, joint_limits: typing.Optional[torch.Tensor] = None, config_sampling_method: typing.Union[str, typing.Callable[[int], torch.Tensor]] = 'uniform', max_iterations: int = 50, lr: float = 0.2, line_search: typing.Optional[rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.LineSearch] = None, regularlization: float = 1e-09, debug=False, early_stopping_any_converged=False, early_stopping_no_improvement='any', early_stopping_no_improvement_patience=2, optimizer_method: typing.Union[str, typing.Type[torch.optim.Optimizer]] = 'sgd') :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.PseudoInverseIK Bases: :py:obj:`rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.InverseKinematics` .. py:method:: compute_dq(J, dx) :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.PseudoInverseIK.compute_dq .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.PseudoInverseIK.compute_dq .. py:method:: solve(target_poses: pytorch_kinematics.transforms.Transform3d) -> rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.IKSolution :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.PseudoInverseIK.solve .. py:class:: PseudoInverseIKWithSVD(serial_chain: pytorch_kinematics.chain.SerialChain, pos_tolerance: float = 0.001, rot_tolerance: float = 0.01, retry_configs: typing.Optional[torch.Tensor] = None, num_retries: typing.Optional[int] = None, joint_limits: typing.Optional[torch.Tensor] = None, config_sampling_method: typing.Union[str, typing.Callable[[int], torch.Tensor]] = 'uniform', max_iterations: int = 50, lr: float = 0.2, line_search: typing.Optional[rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.LineSearch] = None, regularlization: float = 1e-09, debug=False, early_stopping_any_converged=False, early_stopping_no_improvement='any', early_stopping_no_improvement_patience=2, optimizer_method: typing.Union[str, typing.Type[torch.optim.Optimizer]] = 'sgd') :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.PseudoInverseIKWithSVD Bases: :py:obj:`rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.PseudoInverseIK` .. py:method:: compute_dq(J, dx) :canonical: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.PseudoInverseIKWithSVD.compute_dq .. autodoc2-docstring:: rofunc.utils.robolab.kinematics.pytorch_kinematics_utils.PseudoInverseIKWithSVD.compute_dq