fastdev.robo.articulation¶

Module Contents¶

Specification for a single articulation.

Parameters:

urdf_or_mjcf_path (str) – Path to the URDF or MJCF file.
mesh_dir (str, optional) – Directory to store mesh files. Defaults to None.
format (str, optional) – Format of the file, either “urdf” or “mjcf”. Defaults to None.
mjcf_assets (Dict[str, Any], optional) – Assets for MJCF files. Defaults to None.
enable_mimic_joints (bool, optional) – Whether to enable mimic joints. Defaults to True.
base_link_name (Optional[str])
ee_link_names (Optional[Union[str, List[str]]])

Examples

>>> arti_spec = ArticulationSpec(urdf_or_mjcf_path="assets/robot_description/panda.urdf")
>>> arti_spec.num_dofs
8

base_link_name: str[source]¶

ee_link_names: List[str][source]¶

link_names: List[str][source]¶

num_links: int[source]¶

num_dofs: int[source]¶

active_joint_names: List[str][source]¶

has_mimic_joints: bool[source]¶

mimic_joint_names: List[str][source]¶

full_joint_names: List[str][source]¶

num_full_joints: int[source]¶

urdf_or_mjcf_path = ''[source]¶

mesh_dir = ''[source]¶

format = None[source]¶

mjcf_assets = None[source]¶

enable_mimic_joints = True[source]¶

num_mimic_joints[source]¶

parse_urdf() → Tuple[Dict[str, Joint], Dict[str, Link]][source]¶

Return type:: Tuple[Dict[str, Joint], Dict[str, Link]]

parse_mjcf() → Tuple[Dict[str, Joint], Dict[str, Link]][source]¶

Return type:: Tuple[Dict[str, Joint], Dict[str, Link]]

__repr__() → str[source]¶

Return type:: str

__str__() → str[source]¶

Return type:: str

property joint_limits: Optional[jaxtyping.Float[numpy.ndarray, num_dofs 2]][source]¶

Return type:: Optional[jaxtyping.Float[numpy.ndarray, num_dofs 2]]

property mimic_joint_limits: Optional[jaxtyping.Float[numpy.ndarray, num_mimic_joints 2]][source]¶

Return type:: Optional[jaxtyping.Float[numpy.ndarray, num_mimic_joints 2]]

property mimic_joint_indices: jaxtyping.Int[numpy.ndarray, num_mimic_joints][source]¶

Return type:: jaxtyping.Int[numpy.ndarray, num_mimic_joints]

property mimic_multipliers: jaxtyping.Float[numpy.ndarray, num_mimic_joints][source]¶

Return type:: jaxtyping.Float[numpy.ndarray, num_mimic_joints]

property mimic_offsets: jaxtyping.Float[numpy.ndarray, num_mimic_joints][source]¶

Return type:: jaxtyping.Float[numpy.ndarray, num_mimic_joints]

property link_joint_indices: jaxtyping.Int[numpy.ndarray, num_links][source]¶

Return type:: jaxtyping.Int[numpy.ndarray, num_links]

property link_indices_topological_order: jaxtyping.Int[numpy.ndarray, num_links][source]¶

Return type:: jaxtyping.Int[numpy.ndarray, num_links]

property parent_link_indices: jaxtyping.Int[numpy.ndarray, num_links][source]¶

Return type:: jaxtyping.Int[numpy.ndarray, num_links]

property link_joint_axes: Optional[jaxtyping.Float[numpy.ndarray, num_links 3]][source]¶

Return type:: Optional[jaxtyping.Float[numpy.ndarray, num_links 3]]

property full_joint_axes: jaxtyping.Float[numpy.ndarray, num_full_joints 3][source]¶

Return type:: jaxtyping.Float[numpy.ndarray, num_full_joints 3]

property link_joint_origins: jaxtyping.Float[numpy.ndarray, num_links 4 4][source]¶

Return type:: jaxtyping.Float[numpy.ndarray, num_links 4 4]

property link_joint_types: jaxtyping.Int[numpy.ndarray, num_links][source]¶

Return type:: jaxtyping.Int[numpy.ndarray, num_links]

get_ancestor_links_mask(link_name_or_idx: str | int) → jaxtyping.Int[numpy.ndarray, num_links][source]¶

Parameters:: link_name_or_idx (Union[str, int])
Return type:: jaxtyping.Int[numpy.ndarray, num_links]

get_link_trimesh_meshes(mode: Literal['visual', 'collision'] = 'collision', return_empty_meshes: bool = True) → Dict[str, trimesh.Trimesh][source]¶

Parameters:

mode (Literal['visual', 'collision'])
return_empty_meshes (bool)

Return type:

Dict[str, trimesh.Trimesh]

class fastdev.robo.articulation.Articulation(specs: ArticulationSpec | Sequence[ArticulationSpec], device: Device = 'cpu')[source]¶

Class to manage multiple articulations.

Parameters:

specs (Sequence[ArticulationSpec]) – Articulation specifications.
device (str, optional) – Device to store tensors. Defaults to “cpu”.

Examples

>>> arti = Articulation(ArticulationSpec(urdf_or_mjcf_path="assets/robot_description/panda.urdf"))
>>> link_poses = arti.forward_kinematics(torch.zeros(1, arti.total_num_dofs), clamp_joint_values=False)
>>> torch.allclose(link_poses[0, -1, :3, 3], torch.tensor([0.0880, 0.0000, 0.8676]), atol=1e-3)
True

specs: Sequence[ArticulationSpec][source]¶

num_arti: int[source]¶

total_num_dofs: int[source]¶

total_num_links: int[source]¶

total_num_full_joints: int[source]¶

device = 'cpu'[source]¶

Parameters:

urdf_or_mjcf_paths (Union[Union[str, pathlib.Path], Sequence[Union[str, pathlib.Path]]])
mesh_dirs (Optional[Union[Union[str, pathlib.Path], Sequence[Union[str, pathlib.Path]]]])
device (Device)

Return type:

Articulation

Keep this method for compatibility with old code.

Parameters:

urdf_or_mjcf_path (Union[Union[str, pathlib.Path], Sequence[Union[str, pathlib.Path]]])
mesh_dir (Optional[Union[Union[str, pathlib.Path], Sequence[Union[str, pathlib.Path]]]])
device (Device)

Return type:

Articulation

property first_spec: ArticulationSpec[source]¶

Return type:: ArticulationSpec

property has_none_joint_limits: bool[source]¶

Return type:: bool

property has_none_mimic_joint_limits: bool[source]¶

Return type:: bool

property has_mimic_joints: bool[source]¶

Return type:: bool

property joint_limits: Optional[jaxtyping.Float[torch.Tensor, total_num_dofs 2]][source]¶

Return type:: Optional[jaxtyping.Float[torch.Tensor, total_num_dofs 2]]

property num_dofs: int[source]¶

Return type:: int

property active_joint_names: List[str] | List[List[str]][source]¶

Return type:: Union[List[str], List[List[str]]]

property mimic_joint_names: List[str] | List[List[str]][source]¶

Return type:: Union[List[str], List[List[str]]]

property link_names: List[str] | List[List[str]][source]¶

Return type:: Union[List[str], List[List[str]]]

get_packed_joint_limits(return_tensors: Literal['np', 'pt'] = 'np', return_mimic_joints: bool = False) → numpy.ndarray | torch.Tensor | None[source]¶

Parameters:

return_tensors (Literal['np', 'pt'])
return_mimic_joints (bool)

Return type:

Optional[Union[numpy.ndarray, torch.Tensor]]

get_packed_link_indices_topological_order(return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:: return_tensors (Literal['np', 'pt'])
Return type:: Union[numpy.ndarray, torch.Tensor]

get_packed_parent_link_indices(return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:: return_tensors (Literal['np', 'pt'])
Return type:: Union[numpy.ndarray, torch.Tensor]

get_packed_link_joint_indices(return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:: return_tensors (Literal['np', 'pt'])
Return type:: Union[numpy.ndarray, torch.Tensor]

get_packed_link_joint_types(return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:: return_tensors (Literal['np', 'pt'])
Return type:: Union[numpy.ndarray, torch.Tensor]

get_packed_link_joint_origins(return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:: return_tensors (Literal['np', 'pt'])
Return type:: Union[numpy.ndarray, torch.Tensor]

get_packed_link_joint_axes(return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:: return_tensors (Literal['np', 'pt'])
Return type:: Union[numpy.ndarray, torch.Tensor]

get_packed_full_joint_axes(return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:: return_tensors (Literal['np', 'pt'])
Return type:: Union[numpy.ndarray, torch.Tensor]

get_joint_first_indices(return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:: return_tensors (Literal['np', 'pt'])
Return type:: Union[numpy.ndarray, torch.Tensor]

get_link_first_indices(return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:: return_tensors (Literal['np', 'pt'])
Return type:: Union[numpy.ndarray, torch.Tensor]

get_packed_ancestor_links_mask(target_link_indices: jaxtyping.Int[torch.Tensor, num_arti], return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:

target_link_indices (jaxtyping.Int[torch.Tensor, num_arti])
return_tensors (Literal['np', 'pt'])

Return type:

Union[numpy.ndarray, torch.Tensor]

get_packed_zero_joint_values(return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:: return_tensors (Literal['np', 'pt'])
Return type:: Union[numpy.ndarray, torch.Tensor]

get_zero_joint_values(return_tensors: Literal['np', 'pt'] = 'np') → numpy.ndarray | torch.Tensor[source]¶

Parameters:: return_tensors (Literal['np', 'pt'])
Return type:: Union[numpy.ndarray, torch.Tensor]

get_link_trimesh_meshes(mode: Literal['visual', 'collision'] = 'collision', return_empty_meshes: bool = False) → Dict[str, trimesh.Trimesh] | List[Dict[str, trimesh.Trimesh]][source]¶

Parameters:

mode (Literal['visual', 'collision'])
return_empty_meshes (bool)

Return type:

Union[Dict[str, trimesh.Trimesh], List[Dict[str, trimesh.Trimesh]]]

apply_mimic_joints(joint_values: jaxtyping.Float[torch.Tensor, ... total_num_dofs], clamp_joint_values: bool = True) → jaxtyping.Float[torch.Tensor, ... total_num_full_joints][source]¶

Parameters:

joint_values (jaxtyping.Float[torch.Tensor, ... total_num_dofs])
clamp_joint_values (bool)

Return type:

jaxtyping.Float[torch.Tensor, … total_num_full_joints]

forward_kinematics(joint_values: Union[jaxtyping.Float[torch.Tensor, ... total_num_dofs]], joint_names: Optional[Union[List[List[str]], List[str]]] = None, root_poses: Optional[Union[jaxtyping.Float[torch.Tensor, ... num_arti 4 4], jaxtyping.Float[torch.Tensor, ... 4 4]]] = None, clamp_joint_values: bool = True, use_warp: bool = True) → jaxtyping.Float[torch.Tensor, ... total_num_links 4 4][source]¶

Forward kinematics.

Parameters:

joint_values (torch.Tensor) – Packed joint values of shape (…, total_num_dofs).
joint_names (List[List[str]], optional) – Joint names for each articulation. Defaults to None. Could be a single list of joint names if there is only one articulation.
root_poses (torch.Tensor, optional) – Root poses of shape (…, num_arti, 4, 4). Defaults to None. The num_arti dimension can be omitted only if a single articulation is being used.
clamp_joint_values (bool, optional) – Whether to clamp joint values to joint limits. Defaults to True.
use_warp (bool)

Return type:

jaxtyping.Float[torch.Tensor, … total_num_links 4 4]

jacobian(joint_values: jaxtyping.Float[torch.Tensor, ... total_num_dofs], joint_names: Optional[Union[List[List[str]], List[str]]] = None, root_poses: Optional[Union[jaxtyping.Float[torch.Tensor, ... num_arti 4 4], jaxtyping.Float[torch.Tensor, ... 4 4]]] = None, target_links: Optional[Union[str, List[str], int, List[int], jaxtyping.Int[torch.Tensor, num_arti]]] = None, clamp_joint_values: bool = True, use_warp: bool = True, return_target_link_poses: bool = False) → Union[jaxtyping.Float[torch.Tensor, ... 6 total_num_dofs], Tuple[jaxtyping.Float[torch.Tensor, ... 6 total_num_dofs], jaxtyping.Float[torch.Tensor, ... num_arti 4 4]]][source]¶

Parameters:

joint_values (jaxtyping.Float[torch.Tensor, ... total_num_dofs])
joint_names (Optional[Union[List[List[str]], List[str]]])
root_poses (Optional[Union[jaxtyping.Float[torch.Tensor, ... num_arti 4 4], jaxtyping.Float[torch.Tensor, ... 4 4]]])
target_links (Optional[Union[str, List[str], int, List[int], jaxtyping.Int[torch.Tensor, num_arti]]])
clamp_joint_values (bool)
use_warp (bool)
return_target_link_poses (bool)

Return type:

Union[jaxtyping.Float[torch.Tensor, … 6 total_num_dofs], Tuple[jaxtyping.Float[torch.Tensor, … 6 total_num_dofs], jaxtyping.Float[torch.Tensor, … num_arti 4 4]]]

inverse_kinematics(target_link_poses: Union[jaxtyping.Float[torch.Tensor, ... num_arti 4 4], jaxtyping.Float[torch.Tensor, ... 4 4]], target_links: Optional[Union[str, List[str], int, List[int], torch.Tensor]] = None, use_warp: bool = False, max_iterations: int = 30, learning_rate: float = 0.2, tolerance: float = 0.001, damping: float = 0.0001, num_retries: int = 50, init_joint_values: Optional[jaxtyping.Float[torch.Tensor, ... total_num_dofs]] = None, jitter_strength: float = 1.0, return_success: bool = False, force_insert_articulation_dim: bool = False) → Union[jaxtyping.Float[torch.Tensor, ... total_num_dofs], Tuple[jaxtyping.Float[torch.Tensor, ... total_num_dofs], jaxtyping.Bool[torch.Tensor, ... num_arti]]][source]¶

Parameters:

target_link_poses (Union[jaxtyping.Float[torch.Tensor, ... num_arti 4 4], jaxtyping.Float[torch.Tensor, ... 4 4]])
target_links (Optional[Union[str, List[str], int, List[int], torch.Tensor]])
use_warp (bool)
max_iterations (int)
learning_rate (float)
tolerance (float)
damping (float)
num_retries (int)
init_joint_values (Optional[jaxtyping.Float[torch.Tensor, ... total_num_dofs]])
jitter_strength (float)
return_success (bool)
force_insert_articulation_dim (bool)

Return type:

Union[jaxtyping.Float[torch.Tensor, … total_num_dofs], Tuple[jaxtyping.Float[torch.Tensor, … total_num_dofs], jaxtyping.Bool[torch.Tensor, … num_arti]]]

apply_mimic_joints_numpy(joint_values: jaxtyping.Float[numpy.ndarray, ... total_num_dofs], clamp_joint_values: bool = True) → jaxtyping.Float[numpy.ndarray, ... total_num_joints][source]¶

Parameters:

joint_values (jaxtyping.Float[numpy.ndarray, ... total_num_dofs])
clamp_joint_values (bool)

Return type:

jaxtyping.Float[numpy.ndarray, … total_num_joints]

forward_kinematics_numpy(joint_values: Union[jaxtyping.Float[numpy.ndarray, ... total_num_dofs]], joint_names: Optional[Union[List[List[str]], List[str]]] = None, root_poses: Optional[Union[jaxtyping.Float[numpy.ndarray, ... num_arti 4 4], jaxtyping.Float[numpy.ndarray, ... 4 4]]] = None, clamp_joint_values: bool = True) → jaxtyping.Float[numpy.ndarray, ... total_num_links 4 4][source]¶

Forward kinematics.

Parameters:

joint_values (torch.Tensor) – Packed joint values of shape (…, total_num_dofs).
joint_names (List[List[str]], optional) – Joint names for each articulation. Defaults to None. Could be a single list of joint names if there is only one articulation.
root_poses (torch.Tensor, optional) – Root poses of shape (…, num_arti, 4, 4). Defaults to None. The num_arti dimension can be omitted only if a single articulation is being used.
clamp_joint_values (bool, optional) – Whether to clamp joint values to joint limits. Defaults to True.

Return type:

jaxtyping.Float[numpy.ndarray, … total_num_links 4 4]

to(device: Device) → Articulation[source]¶

Parameters:: device (Device)
Return type:: Articulation

__len__() → int[source]¶

Return type:: int

__getitem__(idx: int | slice) → Articulation[source]¶

Parameters:: idx (Union[int, slice])
Return type:: Articulation

__repr__() → str[source]¶

Return type:: str

__str__() → str[source]¶

Return type:: str