adam.parametric.pytorch ======================= .. py:module:: adam.parametric.pytorch Submodules ---------- .. toctree:: :maxdepth: 1 /autoapi/adam/parametric/pytorch/computations_parametric/index Classes ------- .. autoapisummary:: adam.parametric.pytorch.KinDynComputationsParametric Package Contents ---------------- .. py:class:: KinDynComputationsParametric(urdfstring: str, joints_name_list: list, links_name_list: list, device: torch.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu'), dtype: torch.dtype = torch.float64, root_link: str = None, gravity: numpy.array = torch.tensor([0, 0, -9.80665, 0, 0, 0])) This is a small class that retrieves robot quantities using Pytorch for Floating Base systems. This is parametric w.r.t the link length and densities. .. py:attribute:: math .. py:attribute:: g .. py:attribute:: links_name_list .. py:attribute:: joints_name_list .. py:attribute:: urdfstring .. py:attribute:: representation .. py:method:: set_frame_velocity_representation(representation: adam.core.constants.Representations) -> None Sets the representation of the velocity of the frames :param representation: The representation of the velocity :type representation: Representations .. py:method:: mass_matrix(base_transform: torch.Tensor, s: torch.Tensor, length_multiplier: torch.Tensor, densities: torch.Tensor) -> torch.Tensor Returns the Mass Matrix functions computed the CRBA :param base_transform: The homogenous transform from base to world frame :type base_transform: torch.tensor :param s: The joints position :type s: torch.tensor :param length_multiplier: The length multiplier of the parametrized links :type length_multiplier: torch.tensor :param densities: The densities of the parametrized links :type densities: torch.tensor :returns: Mass Matrix :rtype: M (torch.tensor) .. py:method:: centroidal_momentum_matrix(base_transform: torch.Tensor, s: torch.Tensor, length_multiplier: torch.Tensor, densities: torch.Tensor) -> torch.Tensor Returns the Centroidal Momentum Matrix functions computed the CRBA :param base_transform: The homogenous transform from base to world frame :type base_transform: torch.tensor :param s: The joints position :type s: torch.tensor :param length_multiplier: The length multiplier of the parametrized links :type length_multiplier: torch.tensor :param densities: The densities of the parametrized links :type densities: torch.tensor :returns: Centroidal Momentum matrix :rtype: Jcc (torch.tensor) .. py:method:: forward_kinematics(frame, base_transform: torch.Tensor, joint_positions: torch.Tensor, length_multiplier: torch.Tensor, densities: torch.Tensor) -> torch.Tensor Computes the forward kinematics relative to the specified frame :param frame: The frame to which the fk will be computed :type frame: str :param base_transform: The homogenous transform from base to world frame :type base_transform: torch.tensor :param joint_positions: The joints position :type joint_positions: torch.tensor :param length_multiplier: The length multiplier of the parametrized links :type length_multiplier: torch.tensor :param densities: The densities of the parametrized links :type densities: torch.tensor :returns: The fk represented as Homogenous transformation matrix :rtype: T_fk (torch.tensor) .. py:method:: jacobian(frame: str, base_transform: torch.Tensor, joint_positions: torch.Tensor, length_multiplier: torch.Tensor, densities: torch.Tensor) -> torch.Tensor Returns the Jacobian relative to the specified frame :param frame: The frame to which the jacobian will be computed :type frame: str :param base_transform: The homogenous transform from base to world frame :type base_transform: torch.tensor :param joint_positions: The joints position :type joint_positions: torch.tensor :param length_multiplier: The length multiplier of the parametrized links :type length_multiplier: torch.tensor :param densities: The densities of the parametrized links :type densities: torch.tensor :returns: The Jacobian relative to the frame :rtype: J_tot (torch.tensor) .. py:method:: relative_jacobian(frame, joint_positions: torch.Tensor, length_multiplier: torch.Tensor, densities: torch.Tensor) -> torch.Tensor Returns the Jacobian between the root link and a specified frame frames :param frame: The tip of the chain :type frame: str :param joint_positions: The joints position :type joint_positions: torch.tensor :param length_multiplier: The length multiplier of the parametrized links :type length_multiplier: torch.tensor :param densities: The densities of the parametrized links :type densities: torch.tensor :returns: The Jacobian between the root and the frame :rtype: J (torch.tensor) .. py:method:: jacobian_dot(frame: str, base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor, length_multiplier: torch.Tensor, densities: torch.Tensor) -> torch.Tensor Returns the Jacobian derivative relative to the specified frame :param frame: The frame to which the jacobian will be computed :type frame: str :param base_transform: The homogenous transform from base to world frame :type base_transform: torch.Tensor :param joint_positions: The joints position :type joint_positions: torch.Tensor :param base_velocity: The base velocity :type base_velocity: torch.Tensor :param joint_velocities: The joint velocities :type joint_velocities: torch.Tensor :param length_multiplier: The length multiplier of the parametrized links :type length_multiplier: torch.tensor :param densities: The densities of the parametrized links :type densities: torch.tensor :returns: The Jacobian derivative relative to the frame :rtype: Jdot (torch.Tensor) .. py:method:: CoM_position(base_transform: torch.Tensor, joint_positions: torch.Tensor, length_multiplier: torch.Tensor, densities: torch.Tensor) -> torch.Tensor Returns the CoM position :param base_transform: The homogenous transform from base to world frame :type base_transform: torch.tensor :param joint_positions: The joints position :type joint_positions: torch.tensor :param length_multiplier: The length multiplier of the parametrized links :type length_multiplier: torch.tensor :param densities: The densities of the parametrized links :type densities: torch.tensor :returns: The CoM position :rtype: com (torch.tensor) .. py:method:: bias_force(base_transform: torch.Tensor, s: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor, length_multiplier: torch.Tensor, densities: torch.Tensor) -> torch.Tensor Returns the bias force of the floating-base dynamics ejoint_positionsuation, using a reduced RNEA (no acceleration and external forces) :param base_transform: The homogenous transform from base to world frame :type base_transform: torch.tensor :param s: The joints position :type s: torch.tensor :param base_velocity: The base velocity :type base_velocity: torch.tensor :param joint_velocities: The joints velocity :type joint_velocities: torch.tensor :param length_multiplier: The length multiplier of the parametrized links :type length_multiplier: torch.tensor :param densities: The densities of the parametrized links :type densities: torch.tensor :returns: the bias force :rtype: h (torch.tensor) .. py:method:: coriolis_term(base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor, length_multiplier: torch.Tensor, densities: torch.Tensor) -> torch.Tensor Returns the coriolis term of the floating-base dynamics ejoint_positionsuation, using a reduced RNEA (no acceleration and external forces) :param base_transform: The homogenous transform from base to world frame :type base_transform: torch.tensor :param joint_positions: The joints position :type joint_positions: torch.tensor :param base_velocity: The base velocity :type base_velocity: torch.tensor :param joint_velocities: The joints velocity :type joint_velocities: torch.tensor :param length_multiplier: The length multiplier of the parametrized links :type length_multiplier: torch.tensor :param densities: The densities of the parametrized links :type densities: torch.tensor :returns: the Coriolis term :rtype: C (torch.tensor) .. py:method:: gravity_term(base_transform: torch.Tensor, base_positions: torch.Tensor, length_multiplier: torch.Tensor, densities: torch.Tensor) -> torch.Tensor Returns the gravity term of the floating-base dynamics ejoint_positionsuation, using a reduced RNEA (no acceleration and external forces) :param base_transform: The homogenous transform from base to world frame :type base_transform: torch.tensor :param base_positions: The joints position :type base_positions: torch.tensor :param length_multiplier: The length multiplier of the parametrized links :type length_multiplier: torch.tensor :param densities: The densities of the parametrized links :type densities: torch.tensor :returns: the gravity term :rtype: G (torch.tensor) .. py:method:: get_total_mass(length_multiplier: torch.Tensor, densities: torch.Tensor) -> float Returns the total mass of the robot :param length_multiplier: The length multiplier of the parametrized links :type length_multiplier: torch.tensor :param densities: The densities of the parametrized links :type densities: torch.tensor :returns: The total mass :rtype: mass .. py:method:: get_original_densities() -> list[float] Returns the original densities of the parametric links :returns: The original densities of the parametric links :rtype: densities