PyTorch interface

PyTorch interface#

This module provides the PyTorch implementation of the Rigid Body Dynamics algorithms.

Tip

For the batched version of the algorithms, see adam.pytorch.computation_batch.

class adam.pytorch.computations.KinDynComputations(urdfstring: str, joints_name_list: list = None, root_link: str = None, gravity: array = tensor([0.0000, 0.0000, -9.8067, 0.0000, 0.0000, 0.0000]))[source]#

Bases: object

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

set_frame_velocity_representation(representation: Representations) → None[source]#

Sets the representation of the velocity of the frames

Parameters:

representation (Representations) – The representation of the velocity

mass_matrix(base_transform: Tensor, joint_positions: Tensor) → 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: Tensor, joint_positions: Tensor) → 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: Tensor, joint_positions: Tensor) → 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: Tensor, joint_positions: Tensor) → 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: Tensor) → 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: Tensor, joint_positions: Tensor, base_velocity: Tensor, joint_velocities: Tensor) → 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: Tensor, joint_positions: Tensor) → 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: Tensor, joint_positions: Tensor) → 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: Tensor, joint_positions: Tensor, base_velocity: Tensor, joint_velocities: Tensor) → 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: Tensor, joint_positions: Tensor, base_velocity: Tensor, joint_velocities: Tensor) → 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: Tensor, joint_positions: Tensor) → 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)

get_total_mass() → float[source]#

Returns the total mass of the robot

Returns:

The total mass

Return type:

mass

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