curobo.types.state module

class curobo.types.state.FilterCoeff(position: 'float' = 0.0, velocity: 'float' = 0.0, acceleration: 'float' = 0.0, jerk: 'float' = 0.0)

Bases: object

Parameters:
  • position (float) –

  • velocity (float) –

  • acceleration (float) –

  • jerk (float) –

position: float = 0.0
velocity: float = 0.0
acceleration: float = 0.0
jerk: float = 0.0
class curobo.types.state.State

Bases: Sequence

blend(coeff, new_state)
Parameters:
to(tensor_args)
Parameters:

tensor_args (TensorDeviceType) –

get_state_tensor()
apply_kernel(kernel_mat)
clone()
_abc_impl = <_abc._abc_data object>
class curobo.types.state.JointState(position: 'Union[List[float], T_DOF]', velocity: 'Union[List[float], T_DOF, None]' = None, acceleration: 'Union[List[float], T_DOF, None]' = None, joint_names: 'Optional[List[str]]' = None, jerk: 'Union[List[float], T_DOF, None]' = None, tensor_args: 'TensorDeviceType' = TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32))

Bases: State

Parameters:
  • position (List[float] | Tensor) –

  • velocity (List[float] | Tensor | None) –

  • acceleration (List[float] | Tensor | None) –

  • joint_names (List[str] | None) –

  • jerk (List[float] | Tensor | None) –

  • tensor_args (TensorDeviceType) –

position: List[float] | Tensor
velocity: List[float] | Tensor | None = None
acceleration: List[float] | Tensor | None = None
joint_names: List[str] | None = None
jerk: List[float] | Tensor | None = None
tensor_args: TensorDeviceType = TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32)
static from_numpy(joint_names, position, velocity=None, acceleration=None, jerk=None, tensor_args=TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32))
Parameters:
  • joint_names (List[str]) –

  • position (np.ndarry) –

  • velocity (Optional[np.ndarray]) –

  • acceleration (Optional[np.ndarray]) –

  • jerk (Optional[np.ndarray]) –

  • tensor_args (TensorDeviceType) –

static from_position(position, joint_names=None)
Parameters:
  • position (Tensor) –

  • joint_names (List[str] | None) –

apply_kernel(kernel_mat)
repeat_seeds(num_seeds)
Parameters:

num_seeds (int) –

to(tensor_args)
Parameters:

tensor_args (TensorDeviceType) –

clone()
blend(coeff, new_state)
Parameters:
get_state_tensor()
static from_state_tensor(state_tensor, joint_names=None, dof=7)
stack(new_state)
Parameters:

new_state (JointState) –

static from_list(position, velocity, acceleration, tensor_args)
Parameters:

tensor_args (TensorDeviceType()) –

copy_at_index(in_joint_state, idx)

Copy joint state to specific index

Parameters:
  • in_joint_state (JointState) – _description_

  • idx (Union[int,torch.Tensor]) – _description_

copy_data(in_joint_state)

Copy data from in_joint_state to self

Parameters:

in_joint_state (JointState) – _description_

_same_shape(new_js)
Parameters:

new_js (JointState) –

copy_(in_joint_state)
Parameters:

in_joint_state (JointState) –

unsqueeze(idx)
Parameters:

idx (int) –

squeeze(dim=0)
Parameters:

dim (int | None) –

calculate_fd_from_position(dt)
Parameters:

dt (Tensor) –

static zeros(size, tensor_args, joint_names=None)
Parameters:
  • size (Tuple[int]) –

  • tensor_args (TensorDeviceType) –

  • joint_names (List[str] | None) –

detach()
get_ordered_joint_state(ordered_joint_names)

Return joint state with a ordered joint names :param ordered_joint_names: _description_ :type ordered_joint_names: List[str]

Returns:

_description_

Return type:

_type_

Parameters:

ordered_joint_names (List[str]) –

inplace_reindex(joint_names)
Parameters:

joint_names (List[str]) –

get_augmented_joint_state(joint_names, lock_joints=None)
Parameters:

lock_joints (JointState | None) –

Return type:

JointState

append_joints(js)
Parameters:

js (JointState) –

trim_trajectory(start_idx, end_idx=None)
Parameters:
  • start_idx (int) –

  • end_idx (int | None) –

scale(dt)
Parameters:

dt (float | Tensor) –

property shape
_abc_impl = <_abc._abc_data object>