adam.pytorch ============ .. py:module:: adam.pytorch Submodules ---------- .. toctree:: :maxdepth: 1 /autoapi/adam/pytorch/computations/index /autoapi/adam/pytorch/torch_like/index Classes ------- .. autoapisummary:: adam.pytorch.KinDynComputations adam.pytorch.KinDynComputationsBatch adam.pytorch.TorchLike Package Contents ---------------- .. py:class:: KinDynComputations(urdfstring: str, joints_name_list: list = None, device: torch.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu'), dtype: torch.dtype = torch.float32, root_link: str = None, gravity: torch.Tensor = torch.tensor([0, 0, -9.80665, 0, 0, 0])) Bases: :py:obj:`adam.model.kindyn_mixin.KinDynFactoryMixin` This is a small class that retrieves robot quantities using Pytorch for Floating Base systems. .. py:attribute:: rbdalgos .. py:attribute:: NDoF .. py:attribute:: g .. 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, joint_positions: 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 joint_positions: The joints position :type joint_positions: torch.tensor :returns: Mass Matrix :rtype: M (torch.tensor) .. py:method:: centroidal_momentum_matrix(base_transform: torch.Tensor, joint_positions: 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 joint_positions: The joints position :type joint_positions: torch.tensor :returns: Centroidal Momentum matrix :rtype: Jcc (torch.tensor) .. py:method:: forward_kinematics(frame, base_transform: torch.Tensor, joint_positions: 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 :returns: The fk represented as Homogenous transformation matrix :rtype: H (torch.tensor) .. py:method:: jacobian(frame: str, base_transform: torch.Tensor, joint_positions: 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 :returns: The Jacobian relative to the frame :rtype: J_tot (torch.tensor) .. py:method:: relative_jacobian(frame, joint_positions: 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 :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) -> 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 :returns: The Jacobian derivative relative to the frame :rtype: Jdot (torch.Tensor) .. py:method:: CoM_position(base_transform: torch.Tensor, joint_positions: 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 :returns: The CoM position :rtype: CoM (torch.tensor) .. py:method:: CoM_jacobian(base_transform: torch.Tensor, joint_positions: torch.Tensor) -> torch.Tensor Returns the CoM Jacobian :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 :returns: The CoM Jacobian :rtype: Jcom (torch.tensor) .. py:method:: bias_force(base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor) -> torch.Tensor Returns the bias force of the floating-base dynamics equation, 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 :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) -> torch.Tensor Returns the coriolis term of the floating-base dynamics equation, 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 :returns: the Coriolis term :rtype: C (torch.tensor) .. py:method:: gravity_term(base_transform: torch.Tensor, joint_positions: torch.Tensor) -> torch.Tensor Returns the gravity term of the floating-base dynamics equation, 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 positions :type joint_positions: torch.tensor :returns: the gravity term :rtype: G (torch.tensor) .. py:method:: aba(base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor, joint_torques: torch.Tensor, external_wrenches: dict[str, torch.Tensor] | None = None) -> torch.Tensor Featherstone Articulated-Body Algorithm (floating base, O(n)). :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 joint_torques: The joint torques :type joint_torques: torch.Tensor :param external_wrenches: External wrenches applied to the robot. Defaults to None. :type external_wrenches: dict[str, torch.Tensor], optional :returns: The base acceleration and the joint accelerations :rtype: torch.Tensor .. py:method:: get_total_mass() -> float Returns the total mass of the robot :returns: The total mass :rtype: mass .. py:class:: KinDynComputationsBatch(urdfstring: str, joints_name_list: list = None, device: torch.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu'), dtype: torch.dtype = torch.float32, root_link: str = None, gravity: torch.Tensor = torch.tensor([0, 0, -9.80665, 0, 0, 0])) Bases: :py:obj:`adam.model.kindyn_mixin.KinDynFactoryMixin` This is a small class that retrieves robot quantities using Pytorch for Floating Base systems. .. py:attribute:: rbdalgos .. py:attribute:: NDoF .. py:attribute:: g .. 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, joint_positions: 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 joint_positions: The joints position :type joint_positions: torch.tensor :returns: Mass Matrix :rtype: M (torch.tensor) .. py:method:: centroidal_momentum_matrix(base_transform: torch.Tensor, joint_positions: 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 joint_positions: The joints position :type joint_positions: torch.tensor :returns: Centroidal Momentum matrix :rtype: Jcc (torch.tensor) .. py:method:: forward_kinematics(frame, base_transform: torch.Tensor, joint_positions: 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 :returns: The fk represented as Homogenous transformation matrix :rtype: H (torch.tensor) .. py:method:: jacobian(frame: str, base_transform: torch.Tensor, joint_positions: 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 :returns: The Jacobian relative to the frame :rtype: J_tot (torch.tensor) .. py:method:: relative_jacobian(frame, joint_positions: 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 :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) -> 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 :returns: The Jacobian derivative relative to the frame :rtype: Jdot (torch.Tensor) .. py:method:: CoM_position(base_transform: torch.Tensor, joint_positions: 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 :returns: The CoM position :rtype: CoM (torch.tensor) .. py:method:: CoM_jacobian(base_transform: torch.Tensor, joint_positions: torch.Tensor) -> torch.Tensor Returns the CoM Jacobian :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 :returns: The CoM Jacobian :rtype: Jcom (torch.tensor) .. py:method:: bias_force(base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor) -> torch.Tensor Returns the bias force of the floating-base dynamics equation, 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 :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) -> torch.Tensor Returns the coriolis term of the floating-base dynamics equation, 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 :returns: the Coriolis term :rtype: C (torch.tensor) .. py:method:: gravity_term(base_transform: torch.Tensor, joint_positions: torch.Tensor) -> torch.Tensor Returns the gravity term of the floating-base dynamics equation, 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 positions :type joint_positions: torch.tensor :returns: the gravity term :rtype: G (torch.tensor) .. py:method:: aba(base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor, joint_torques: torch.Tensor, external_wrenches: dict[str, torch.Tensor] | None = None) -> torch.Tensor Featherstone Articulated-Body Algorithm (floating base, O(n)). :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 joint_torques: The joint torques :type joint_torques: torch.Tensor :param external_wrenches: External wrenches applied to the robot. Defaults to None. :type external_wrenches: dict[str, torch.Tensor], optional :returns: The base acceleration and the joint accelerations :rtype: torch.Tensor .. py:method:: get_total_mass() -> float Returns the total mass of the robot :returns: The total mass :rtype: mass .. py:class:: TorchLike Bases: :py:obj:`adam.core.array_api_math.ArrayAPILike` Class wrapping pyTorch types .. py:attribute:: array :type: torch.Tensor