adam.pytorch

adam.pytorch#

Submodules#

Classes#

KinDynComputationsBatch

A PyTorch-based class for batch kinematic and dynamic computations on robotic systems.

KinDynComputations

This is a small class that retrieves robot quantities using Pytorch for Floating Base systems.

TorchLike

Class wrapping pyTorch types

Package Contents#

class adam.pytorch.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.as_tensor([0, 0, -9.80665, 0, 0, 0]))[source]#

Bases: adam.model.kindyn_mixin.KinDynFactoryMixin

A PyTorch-based class for batch kinematic and dynamic computations on robotic systems.

Provides efficient processing of robot kinematics and dynamics calculations using PyTorch tensors. Supports GPU acceleration and automatic differentiation for robotic applications.

rbdalgos#
NDoF#
g#
funcs#
set_frame_velocity_representation(representation: adam.core.constants.Representations) → None[source]#

Sets the representation of the velocity of the frames

Parameters:

representation (Representations) – The representation of the velocity

mass_matrix(base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the Mass Matrix functions computed the CRBA

Parameters:

  • base_transform (torch.Tensor) – The batch of homogenous transforms from base to world frame

  • joint_positions (torch.Tensor) – The batch of joints position

Returns:

The batch Mass Matrix

Return type:

M (torch.Tensor)

centroidal_momentum_matrix(base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the Centroidal Momentum Matrix functions computed the CRBA

Parameters:

  • base_transform (torch.Tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.Tensor) – The joints position

Returns:

Centroidal Momentum matrix

Return type:

Jcc (torch.Tensor)

relative_jacobian(frame: str, joint_positions: torch.Tensor) → torch.Tensor[source]#
jacobian_dot(frame: str, base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor) → torch.Tensor[source]#

Returns the Jacobian derivative relative to the specified frame

Parameters:

  • frame (str) – The frame to which the jacobian will be computed

  • base_transform (torch.Tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.Tensor) – The joints position

  • base_velocity (torch.Tensor) – The base velocity

  • joint_velocities (torch.Tensor) – The joint velocities

Returns:

The Jacobian derivative relative to the frame

Return type:

Jdot (torch.Tensor)

forward_kinematics(frame: str, base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Computes the forward kinematics between the root and the specified frame

Parameters:

frame (str) – The frame to which the fk will be computed

Returns:

The fk represented as Homogenous transformation matrix

Return type:

H (torch.Tensor)

jacobian(frame: str, base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the Jacobian relative to the specified frame

Parameters:

  • frame (str) – The frame to which the jacobian will be computed

  • base_transform (torch.Tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.Tensor) – The joints position

Returns:

The Jacobian between the root and the frame

Return type:

J (torch.Tensor)

bias_force(base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor) → torch.Tensor[source]#

Returns the bias force of the floating-base dynamics equation, using a reduced RNEA (no acceleration and external forces)

Parameters:

  • base_transform (torch.Tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.Tensor) – The joints position

  • base_velocity (torch.Tensor) – The base velocity

  • joint_velocities (torch.Tensor) – The joints velocity

Returns:

the bias force

Return type:

h (torch.Tensor)

coriolis_term(base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor) → torch.Tensor[source]#

Returns the coriolis term of the floating-base dynamics equation, using a reduced RNEA (no acceleration and external forces)

Parameters:

  • base_transform (torch.Tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.Tensor) – The joints position

  • base_velocity (torch.Tensor) – The base velocity

  • joint_velocities (torch.Tensor) – The joints velocity

Returns:

the Coriolis term

Return type:

C (torch.Tensor)

gravity_term(base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the gravity term of the floating-base dynamics equation, using a reduced RNEA (no acceleration and external forces)

Parameters:

  • base_transform (torch.Tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.Tensor) – The joints position

Returns:

the gravity term

Return type:

G (jnp.array)

CoM_position(base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the CoM position

Parameters:

  • base_transform (torch.Tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.Tensor) – The joints position

Returns:

The CoM position

Return type:

CoM (torch.Tensor)

CoM_jacobian(base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the CoM Jacobian

Parameters:

  • base_transform (torch.Tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.Tensor) – The joints position

Returns:

The CoM Jacobian

Return type:

Jcom (torch.Tensor)

get_total_mass() → float[source]#

Returns the total mass of the robot

Returns:

The total mass

Return type:

mass

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[source]#

Featherstone Articulated-Body Algorithm (floating base, O(n)).

Parameters:

  • base_transform (torch.Tensor) – The batch of homogenous transforms from base to world frame

  • joint_positions (torch.Tensor) – The batch of joints position

  • base_velocity (torch.Tensor) – The batch of base velocity

  • joint_velocities (torch.Tensor) – The batch of joint velocities

  • joint_torques (torch.Tensor) – The batch of joint torques

  • external_wrenches (dict[str, torch.Tensor], optional) – External wrenches applied to the robot. Defaults to None.

Returns:

The batch of base acceleration and the joint accelerations

Return type:

torch.Tensor

class adam.pytorch.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: numpy.array = torch.tensor([0, 0, -9.80665, 0, 0, 0]))[source]#

Bases: adam.model.kindyn_mixin.KinDynFactoryMixin

This is a small class that retrieves robot quantities using Pytorch for Floating Base systems.

rbdalgos#
NDoF#
g#
set_frame_velocity_representation(representation: adam.core.constants.Representations) → None[source]#

Sets the representation of the velocity of the frames

Parameters:

representation (Representations) – The representation of the velocity

mass_matrix(base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the Mass Matrix functions computed the CRBA

Parameters:

  • base_transform (torch.tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.tensor) – The joints position

Returns:

Mass Matrix

Return type:

M (torch.tensor)

centroidal_momentum_matrix(base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the Centroidal Momentum Matrix functions computed the CRBA

Parameters:

  • base_transform (torch.tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.tensor) – The joints position

Returns:

Centroidal Momentum matrix

Return type:

Jcc (torch.tensor)

forward_kinematics(frame, base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Computes the forward kinematics relative to the specified frame

Parameters:

  • frame (str) – The frame to which the fk will be computed

  • base_transform (torch.tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.tensor) – The joints position

Returns:

The fk represented as Homogenous transformation matrix

Return type:

H (torch.tensor)

jacobian(frame: str, base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the Jacobian relative to the specified frame

Parameters:

  • frame (str) – The frame to which the jacobian will be computed

  • base_transform (torch.tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.tensor) – The joints position

Returns:

The Jacobian relative to the frame

Return type:

J_tot (torch.tensor)

relative_jacobian(frame, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the Jacobian between the root link and a specified frame frames

Parameters:

  • frame (str) – The tip of the chain

  • joint_positions (torch.tensor) – The joints position

Returns:

The Jacobian between the root and the frame

Return type:

J (torch.tensor)

jacobian_dot(frame: str, base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor) → torch.Tensor[source]#

Returns the Jacobian derivative relative to the specified frame

Parameters:

  • frame (str) – The frame to which the jacobian will be computed

  • base_transform (torch.Tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.Tensor) – The joints position

  • base_velocity (torch.Tensor) – The base velocity

  • joint_velocities (torch.Tensor) – The joint velocities

Returns:

The Jacobian derivative relative to the frame

Return type:

Jdot (torch.Tensor)

CoM_position(base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the CoM position

Parameters:

  • base_transform (torch.tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.tensor) – The joints position

Returns:

The CoM position

Return type:

CoM (torch.tensor)

CoM_jacobian(base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the CoM Jacobian

Parameters:

  • base_transform (torch.tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.tensor) – The joints position

Returns:

The CoM Jacobian

Return type:

Jcom (torch.tensor)

bias_force(base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor) → torch.Tensor[source]#

Returns the bias force of the floating-base dynamics equation, using a reduced RNEA (no acceleration and external forces)

Parameters:

  • base_transform (torch.tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.tensor) – The joints position

  • base_velocity (torch.tensor) – The base velocity

  • joint_velocities (torch.tensor) – The joints velocity

Returns:

the bias force

Return type:

h (torch.tensor)

coriolis_term(base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor) → torch.Tensor[source]#

Returns the coriolis term of the floating-base dynamics equation, using a reduced RNEA (no acceleration and external forces)

Parameters:

  • base_transform (torch.tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.tensor) – The joints position

  • base_velocity (torch.tensor) – The base velocity

  • joint_velocities (torch.tensor) – The joints velocity

Returns:

the Coriolis term

Return type:

C (torch.tensor)

gravity_term(base_transform: torch.Tensor, joint_positions: torch.Tensor) → torch.Tensor[source]#

Returns the gravity term of the floating-base dynamics equation, using a reduced RNEA (no acceleration and external forces)

Parameters:

  • base_transform (torch.tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.tensor) – The joints positions

Returns:

the gravity term

Return type:

G (torch.tensor)

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[source]#

Featherstone Articulated-Body Algorithm (floating base, O(n)).

Parameters:

  • base_transform (torch.Tensor) – The homogenous transform from base to world frame

  • joint_positions (torch.Tensor) – The joints position

  • base_velocity (torch.Tensor) – The base velocity

  • joint_velocities (torch.Tensor) – The joint velocities

  • joint_torques (torch.Tensor) – The joint torques

  • external_wrenches (dict[str, torch.Tensor], optional) – External wrenches applied to the robot. Defaults to None.

Returns:

The base acceleration and the joint accelerations

Return type:

torch.Tensor

get_total_mass() → float[source]#

Returns the total mass of the robot

Returns:

The total mass

Return type:

mass

class adam.pytorch.TorchLike[source]#

Bases: adam.core.array_api_math.ArrayAPILike

Class wrapping pyTorch types

array: torch.Tensor#
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