adam.pytorch.computation_batch

adam.pytorch.computation_batch#

Classes#

KinDynComputationsBatch

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

Module Contents#

class adam.pytorch.computation_batch.KinDynComputationsBatch(urdfstring: str, joints_name_list: list = None, root_link: str = None, gravity: numpy.array = jnp.array([0, 0, -9.80665, 0, 0, 0]))[source]#

This is a small class that retrieves robot quantities using Jax for Floating Base systems. These functions are vmapped and jit compiled and passed to jax2torch to convert them to PyTorch functions.

rbdalgos[source]#
NDoF[source]#
g[source]#
funcs[source]#
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)

mass_matrix_fun()[source]#

Returns the Mass Matrix functions computed the CRBA as a pytorch function

Returns:

Mass Matrix

Return type:

M (pytorch function)

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)

centroidal_momentum_matrix_fun()[source]#

Returns the Centroidal Momentum Matrix functions computed the CRBA as a pytorch function

Returns:

Centroidal Momentum matrix

Return type:

Jcc (pytorch function)

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

Returns the Jacobian between the root link and a specified frame frames as a pytorch function

Parameters:

frame (str) – The tip of the chain

Returns:

The Jacobian between the root and the frame

Return type:

J (pytorch function)

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)

jacobian_dot_fun(frame: str)[source]#

Returns the Jacobian derivative between the root and the specified frame as a pytorch function

Parameters:

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

Returns:

The Jacobian derivative between the root and the frame

Return type:

Jdot (pytorch function)

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)

forward_kinematics_fun(frame: str)[source]#

Computes the forward kinematics between the root and the specified frame as a pytorch function

Parameters:

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

Returns:

The fk represented as Homogenous transformation matrix

Return type:

H (pytorch function)

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)

jacobian_fun(frame: str)[source]#

Returns the Jacobian relative to the specified frame as a pytorch function

Parameters:

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

Returns:

The Jacobian relative to the frame

Return type:

J (pytorch function)

bias_force(base_transform: torch.Tensor, joint_positions: torch.Tensor, base_velocity: torch.Tensor, joint_velocities: torch.Tensor) → jax.numpy.array[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)

bias_force_fun()[source]#

Returns the bias force of the floating-base dynamics equation as a pytorch function

Returns:

the bias force

Return type:

h (pytorch function)

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)

coriolis_term_fun()[source]#

Returns the coriolis term of the floating-base dynamics equation as a pytorch function

Returns:

the Coriolis term

Return type:

C (pytorch function)

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)

gravity_term_fun()[source]#

Returns the gravity term of the floating-base dynamics equation as a pytorch function

Returns:

the gravity term

Return type:

G (pytorch function)

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_position_fun()[source]#

Returns the CoM position as a pytorch function

Returns:

The CoM position

Return type:

CoM (pytorch function)

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)

CoM_jacobian_fun()[source]#

Returns the CoM Jacobian as a pytorch function

Returns:

The CoM Jacobian

Return type:

Jcom (pytorch function)

get_total_mass() → float[source]#

Returns the total mass of the robot

Returns:

The total mass

Return type:

mass

Contents