reference/generated/torchrl.envs.transforms.SatelliteAttitudeTransform

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SatelliteAttitudeTransform

class torchrl.envs.transforms.SatelliteAttitudeTransform(*args: Any, execute: bool = False, multi_action_dim: int = 1, stack_rewards: bool = True, stack_observations: bool = False, **kwargs: Any)

Expand desired satellite attitudes into CMG gimbal-rate sequences.

This transform is a satellite-specific preset. The policy-facing action is a desired attitude quaternion, provided either as a raw tensor under action_key, under (action_key, "target") / (action_key, "attitude"), or through a SatelliteMacroAction (which also carries per-action durations). The transform computes the current attitude error, applies a small proportional-derivative steering law in body-rate coordinates, maps it through the instantaneous CMG Jacobian, and delegates fixed-length interpolation / execution to MacroPrimitiveTransform.

Parameters:

• num_cmgs – 4 for the pyramid CMG cluster or 6 for the orthogonal cluster.

• action_scale – scale used by SatelliteEnv to map normalized actions to physical gimbal rates. If None, the transform tries to read action_scale from its parent env and falls back to 3.0.

• attitude_gain – proportional gain applied to the quaternion log error.

• angular_rate_gain – damping gain applied to bus_omega.

• jacobian_rotor_h – rotor-momentum scale used in the steering Jacobian.

Examples

>>> import torch
>>> from tensordict import TensorDict
>>> from torchrl.envs import SatelliteAttitudeTransform
>>> td = TensorDict({
...     "action": torch.tensor([[1.0, 0.0, 0.0, 0.0]]),
...     "bus_quat": torch.tensor([[1.0, 0.0, 0.0, 0.0]]),
...     "bus_omega": torch.zeros(1, 3),
...     "gimbal_angles": torch.cat([torch.zeros(1, 4), torch.ones(1, 4)], -1),
... }, batch_size=[1])
>>> SatelliteAttitudeTransform(num_cmgs=4, macro_steps=2, settle_steps=0).inv(td)["action"].shape
torch.Size([1, 2, 4])

attitude_action_target(tensordict: TensorDictBase, target_quat: Tensor) → Tensor

Compute the normalized gimbal-rate target for target_quat.

current_action(tensordict: TensorDictBase, batch_shape: Size, device: device, dtype: dtype, action_dim: int) → Tensor

Return the low-level action used as the interpolation start.

The base implementation starts every macro from the zero action: in the inverse path action_key carries the incoming macro action (the target), so it must not be read back here as the start. Subclasses that can read the controlled state from observations (e.g. joint positions) override this hook.

transform_input_spec(input_spec: Composite) → Composite

Transforms the input spec such that the resulting spec matches transform mapping.

Parameters:

input_spec (TensorSpec) – spec before the transform

Returns:

expected spec after the transform