# Copyright (C) Istituto Italiano di Tecnologia (IIT). All rights reserved.
import warnings
import numpy as np
import torch
from adam.core.constants import Representations
from adam.core.rbd_algorithms import RBDAlgorithms
from adam.pytorch.torch_like import SpatialMath
from adam.model import Model, build_model_factory
from adam.model.kindyn_mixin import KinDynFactoryMixin
from adam.core.array_api_math import spec_from_reference
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class KinDynComputationsBatch(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.
"""
def __init__(
self,
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]),
) -> None:
"""
Args:
urdfstring (str): path/string of a URDF or a MuJoCo MjModel.
NOTE: The parameter name `urdfstring` is deprecated and will be renamed to `model` in a future release.
joints_name_list (list): list of the actuated joints
root_link (str, optional): Deprecated. The root link is automatically chosen as the link with no parent in the URDF. Defaults to None.
"""
ref = torch.tensor(0.0, dtype=dtype, device=device)
math = SpatialMath(spec_from_reference(ref))
factory = build_model_factory(description=urdfstring, math=math)
model = Model.build(factory=factory, joints_name_list=joints_name_list)
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self.rbdalgos = RBDAlgorithms(model=model, math=math)
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self.NDoF = self.rbdalgos.NDoF
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self.g = gravity.to(dtype=dtype, device=device)
if root_link is not None:
warnings.warn(
"The root_link argument is not used. The root link is automatically chosen as the link with no parent in the URDF",
DeprecationWarning,
stacklevel=2,
)
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def set_frame_velocity_representation(
self, representation: Representations
) -> None:
"""Sets the representation of the velocity of the frames
Args:
representation (Representations): The representation of the velocity
"""
self.rbdalgos.set_frame_velocity_representation(representation)
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def mass_matrix(
self, base_transform: torch.Tensor, joint_positions: torch.Tensor
) -> torch.Tensor:
"""Returns the Mass Matrix functions computed the CRBA
Args:
base_transform (torch.Tensor): The batch of homogenous transforms from base to world frame
joint_positions (torch.Tensor): The batch of joints position
Returns:
M (torch.Tensor): The batch Mass Matrix
"""
M, _ = self.rbdalgos.crba(base_transform, joint_positions)
return M.array
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def centroidal_momentum_matrix(
self, base_transform: torch.Tensor, joint_positions: torch.Tensor
) -> torch.Tensor:
"""Returns the Centroidal Momentum Matrix functions computed the CRBA
Args:
base_transform (torch.Tensor): The homogenous transform from base to world frame
joint_positions (torch.Tensor): The joints position
Returns:
Jcc (torch.Tensor): Centroidal Momentum matrix
"""
_, Jcm = self.rbdalgos.crba(base_transform, joint_positions)
return Jcm.array
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def relative_jacobian(
self, frame: str, joint_positions: torch.Tensor
) -> torch.Tensor:
return self.rbdalgos.relative_jacobian(frame, joint_positions).array
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def jacobian_dot(
self,
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
Args:
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:
Jdot (torch.Tensor): The Jacobian derivative relative to the frame
"""
return self.rbdalgos.jacobian_dot(
frame, base_transform, joint_positions, base_velocity, joint_velocities
).array
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def forward_kinematics(
self, frame: str, base_transform: torch.Tensor, joint_positions: torch.Tensor
) -> torch.Tensor:
"""Computes the forward kinematics between the root and the specified frame
Args:
frame (str): The frame to which the fk will be computed
Returns:
H (torch.Tensor): The fk represented as Homogenous transformation matrix
"""
return self.rbdalgos.forward_kinematics(
frame, base_transform, joint_positions
).array
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def jacobian(
self, frame: str, base_transform: torch.Tensor, joint_positions: torch.Tensor
) -> torch.Tensor:
"""Returns the Jacobian relative to the specified frame
Args:
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:
J (torch.Tensor): The Jacobian between the root and the frame
"""
return self.rbdalgos.jacobian(frame, base_transform, joint_positions).array
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def bias_force(
self,
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)
Args:
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:
h (torch.Tensor): the bias force
"""
gravity = self.g
return self.rbdalgos.rnea(
base_transform,
joint_positions,
base_velocity,
joint_velocities,
gravity,
).array.squeeze()
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def coriolis_term(
self,
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)
Args:
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:
C (torch.Tensor): the Coriolis term
"""
gravity = torch.zeros(
(6,),
dtype=base_transform.dtype,
device=base_transform.device,
)
return self.rbdalgos.rnea(
base_transform,
joint_positions,
base_velocity,
joint_velocities,
gravity,
).array.squeeze()
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def gravity_term(
self, 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)
Args:
base_transform (torch.Tensor): The homogenous transform from base to world frame
joint_positions (torch.Tensor): The joints position
Returns:
G (jnp.array): the gravity term
"""
batch_size = base_transform.shape[:-2]
base_velocity = torch.zeros(
batch_size + (6,),
dtype=base_transform.dtype,
device=base_transform.device,
)
joint_velocities = torch.zeros_like(joint_positions)
return self.rbdalgos.rnea(
base_transform,
joint_positions,
base_velocity,
joint_velocities,
self.g,
).array.squeeze()
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def CoM_position(
self, base_transform: torch.Tensor, joint_positions: torch.Tensor
) -> torch.Tensor:
"""Returns the CoM position
Args:
base_transform (torch.Tensor): The homogenous transform from base to world frame
joint_positions (torch.Tensor): The joints position
Returns:
CoM (torch.Tensor): The CoM position
"""
return self.rbdalgos.CoM_position(base_transform, joint_positions).array
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def CoM_jacobian(
self, base_transform: torch.Tensor, joint_positions: torch.Tensor
) -> torch.Tensor:
"""Returns the CoM Jacobian
Args:
base_transform (torch.Tensor): The homogenous transform from base to world frame
joint_positions (torch.Tensor): The joints position
Returns:
Jcom (torch.Tensor): The CoM Jacobian
"""
return self.rbdalgos.CoM_jacobian(base_transform, joint_positions).array
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def get_total_mass(self) -> float:
"""Returns the total mass of the robot
Returns:
mass: The total mass
"""
return self.rbdalgos.get_total_mass()
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def aba(
self,
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)).
Args:
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:
torch.Tensor: The batch of base acceleration and the joint accelerations
"""
return self.rbdalgos.aba(
base_transform,
joint_positions,
base_velocity,
joint_velocities,
joint_torques,
self.g,
external_wrenches,
).array.squeeze()