Kind & Element Names

kind

The kind field describes the semantic role of a tensor — what physical quantity or command it represents. LEAPP provides two separate enums for inputs and outputs to clearly distinguish between observed state and commanded targets. kind may also be any plain string when you need a custom semantic label.

InputKindEnum

Used with input_tensors(). These represent observed state or commanded references flowing into a node.

Enum value	YAML string	Description
JOINT_POSITION	state/joint/position	Observed joint positions (e.g. encoder readings)
JOINT_VELOCITY	state/joint/velocity	Observed joint velocities
JOINT_EFFORT	state/joint/effort	Observed joint effort
BODY_POSE	state/body/pose	Observed body pose
BODY_POSITION	state/body/position	Observed body position
BODY_VEL	state/body/velocity	Observed body velocity
BODY_ACC	state/body/acceleration	Observed body acceleration
BODY_LINEAR_ACCELERATION	state/body/linear_acceleration	Body linear acceleration (e.g. from IMU)
BODY_LINEAR_VELOCITY	state/body/linear_velocity	Body linear velocity
BODY_ANGULAR_ACCELERATION	state/body/angular_acceleration	Body angular acceleration
BODY_ANGULAR_VELOCITY	state/body/angular_velocity	Body angular velocity (e.g. gyroscope)
BODY_ROTATION	state/body/rotation	Body rotation / orientation
WRENCH	state/wrench	Observed wrench values
VECTOR3D	state/vector3d	Generic 3D vector state
COMMAND_JOINT_POSITION	command/joint/position	Commanded joint position reference
COMMAND_JOINT_VELOCITY	command/joint/velocity	Commanded joint velocity reference
COMMAND_BODY_ROTATION	command/body/rotation	Commanded body rotation reference
COMMAND_BODY_VELOCITY	command/body/velocity	Commanded body velocity reference
COMMAND_POSE	command/body/pose	Commanded body pose reference
COMMAND_JOINT_TORQUES	command/joint/torques	Commanded joint torques reference

from leapp.utils.enums import InputKindEnum

TensorSemantics("joint_pos", tensor, kind=InputKindEnum.JOINT_POSITION)
TensorSemantics("imu_gyro", tensor,
                kind=InputKindEnum.BODY_ANGULAR_VELOCITY)
TensorSemantics("target_pos", tensor,
                kind=InputKindEnum.COMMAND_JOINT_POSITION)

# Custom string kinds are also allowed.
TensorSemantics("terrain_latent", tensor,
                kind="state/environment/terrain_embedding")

OutputKindEnum

Used with output_tensors(). These represent target commands or control outputs produced by a node.

Enum value	YAML string	Description
KP	kp	Proportional gain
KD	kd	Derivative gain
JOINT_POSITION	target/joint/position	Target joint position
JOINT_VELOCITY	target/joint/velocity	Target joint velocity
JOINT_TORQUES	target/joint/torques	Target joint torques
JOINT_EFFORT	target/joint/effort	Target joint effort
BODY_POSITION	target/body/position	Target body position
BODY_LINEAR_ACCELERATION	target/body/linear_acceleration	Target body linear acceleration
BODY_ORIENTATION	target/body/orientation	Target body orientation
BODY_LINEAR_VELOCITY	target/body/linear_velocity	Target body linear velocity
BODY_ANGULAR_ACCELERATION	target/body/angular_acceleration	Target body angular acceleration

from leapp.utils.enums import OutputKindEnum

TensorSemantics("torques", action, kind=OutputKindEnum.JOINT_TORQUES)
TensorSemantics("kp_gains", kp, kind=OutputKindEnum.KP)

Note

LEAPP does not enforce using InputKindEnum only for inputs or OutputKindEnum only for outputs, but it is strongly recommended to follow this convention. The kind field accepts enum values and plain strings.

element_names

The element_names field provides human-readable names for the elements along each dimension of a tensor. The canonical format is list[list[str]], where the outer list corresponds to tensor dimensions and each inner list names the elements in that dimension. LEAPP also accepts several shorthand formats and normalizes them automatically:

Input format	Normalized to	Use case
"hip"	[["hip"]]	Single named element
["hip", "knee", "ankle"]	[["hip", "knee", "ankle"]]	Flat list — names for one dimension
[["batch"], ["x", "y", "z"]]	[["batch"], ["x", "y", "z"]]	Already canonical — per-dimension names
[None, None, ["r", "g", "b"]]	[None, None, ["r", "g", "b"]]	Partial — only name specific dimensions

# Name elements along the last dimension.
TensorSemantics(
    "joint_pos", tensor,
    element_names=["hip", "knee", "ankle",
                   "shoulder", "elbow", "wrist"])

# Name elements per dimension (e.g. for a [batch, 3] tensor).
TensorSemantics(
    "position", tensor,
    element_names=[None, ["x", "y", "z"]])

# Name a single element.
TensorSemantics("gravity", tensor, element_names="z")

See Tensor Semantics Usage for general TensorSemantics usage patterns and Temporal Semantics for temporal axis metadata.