unilab.envs.mdp.commands.pose_command.UniformPoseCommand

class unilab.envs.mdp.commands.pose_command.UniformPoseCommand[source]

Bases: CommandTerm

Uniformly sampled vector command held until the next resample.

Parameters:

Methods

__init__(cfg, env)

compute(dt[, env_ids])

Advance the command state by dt.

post_compute()

Refresh state that depends on committed command-side simulation writes.

reset(env_ids)

Resets the manager term.

Attributes

command

name

num_envs

cfg

cfg: UniformPoseCommandCfg
__init__(cfg, env)[source]
Parameters:
property command: ndarray
__call__(*args, **kwargs)

Returns the value of the term required by the manager.

Return type:

Any

compute(dt, env_ids=None)

Advance the command state by dt.

With env_ids=None (the per-step path) all envs are updated; with env_ids (the reset path) timers and the command update are scoped to those envs. Metrics are refreshed each call; terms may scope per-row metric work to env_ids since other rows are unchanged since the per-step update, or defer row-wise metric work to reset() when reset is the only consumer (e.g. MotionCommand, issue #1355).

dt may be a scalar (all envs) or a per-env tensor (auto-reset path, where freshly reset envs get zero to keep their timers full). A tensor dt requires env_ids=None.

Parameters:
Return type:

None

property name: str
property num_envs: int
post_compute()

Refresh state that depends on committed command-side simulation writes.

Return type:

None

reset(env_ids)

Resets the manager term.

Parameters:

env_ids (ndarray | slice | None)

Return type:

dict[str, float]

metrics: dict[str, ndarray]