PositionWithinLimitsActionManager#
- class genesis_forge.managers.action.PositionWithinLimitsActionManager(env: GenesisEnv, joint_names: list[str] | str = '.*', default_pos: dict[str, T] | T = {'.*': 0.0}, pd_kp: dict[str, T] | T = None, pd_kv: dict[str, T] | T = None, max_force: dict[str, T] | T = None, damping: dict[str, T] | T = None, stiffness: dict[str, T] | T = None, frictionloss: dict[str, T] | T = None, noise_scale: float = 0.0, action_handler: Callable[[torch.Tensor], None] = None, quiet_action_errors: bool = False)[source]#
Bases:
PositionActionManagerThis is similar to PositionActionManager but converts actions from the range -1.0 - 1.0 to DOF positions within the limits of the actuators.
- Parameters:
env – The environment to manage the DOF actuators for.
joint_names – The joint names to manage.
default_pos – The default DOF positions.
pd_kp – The PD kp values.
pd_kv – The PD kv values.
max_force – The max force values.
damping – The damping values.
stiffness – The stiffness values.
frictionloss – The frictionloss values.
reset_random_scale – Scale all DOF values on reset by this amount +/-.
action_handler – A function to handle the actions.
quiet_action_errors – Whether to quiet action errors.
randomization_cfg – The randomization configuration used to randomize the DOF values across all environments and between resets.
resample_randomization_s – The time interval to resample the randomization values.
Example:
class MyEnv(ManagedEnvironment): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def config(self): self.action_manager = PositionalActionManager( self, joint_names=".*", default_pos={ # Hip joints "Leg[1-2]_Hip": -1.0, "Leg[3-4]_Hip": 1.0, # Femur joints "Leg[1-4]_Femur": 0.5, # Tibia joints "Leg[1-4]_Tibia": 0.6, }, pd_kp={".*": 50}, pd_kv={".*": 0.5}, max_force={".*": 8.0}, ) @property def action_space(self): return self.action_manager.action_space
- get_actions() torch.Tensor#
Get the current actions for the environments.
- get_dofs_force(noise: float = 0.0, clip_to_max_force: bool = False)#
Return the force experienced by the enabled DOFs. This is a wrapper for RigidEntity.get_dofs_force.
- Parameters:
noise – The maximum amount of random noise to add to the force values returned.
clip_to_max_force – Clip the force returned to the maximum force defined by the max_force parameter.
- Returns:
The force experienced by the enabled DOFs.
- get_dofs_position(noise: float = 0.0)#
Return the current position of the enabled DOFs. This is a wrapper for RigidEntity.get_dofs_position.
- Parameters:
noise – The maximum amount of random noise to add to the position values returned.
- get_dofs_velocity(noise: float = 0.0, clip: tuple[float, float] = None)#
Return the current velocity of the enabled DOFs. This is a wrapper for RigidEntity.get_dofs_velocity.
- Parameters:
noise – The maximum amount of random noise to add to the velocity values returned.
clip – Clip the velocity returned.
- handle_actions(actions: torch.Tensor) torch.Tensor[source]#
Converts the actions to position commands, and send them to the DOF actuators. Override this function if you want to change the action handling logic.
- Parameters:
actions – The incoming step actions to handle.
- Returns:
The processed and handled actions.
- step(actions: torch.Tensor) torch.Tensor#
Take the incoming actions for this step and handle them.
- Parameters:
actions – The incoming step actions to handle.
- property action_space: tuple[float, float]#
Returns the actions space for the environment, based on the number of DOFs defined in this action manager.
- property actions: torch.Tensor#
The actions for for the current step.
- property default_dofs_pos: torch.Tensor#
Return the default DOF positions.
- property raw_actions: torch.Tensor#
The actions received from the policy, before being converted.