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docs: update document in response to Simplify configs PR (#1596)

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* docs: update document input/output_shapes -> input/output_features

* fix inconsistent quote (suggested by copilot reviewer)

* docs: shapes => PolicyFeature

* docs: relfect normalization_mapping and remove outdated
This commit is contained in:
Hirokazu Ishida
2026-02-03 04:05:58 +09:00
committed by GitHub
parent b18cef2e26
commit 9c24a09665
5 changed files with 34 additions and 73 deletions
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src/lerobot/policies/diffusion/configuration_diffusion.py
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@@ -30,7 +30,7 @@ class DiffusionConfig(PreTrainedConfig):
Defaults are configured for training with PushT providing proprioceptive and single camera observations.
The parameters you will most likely need to change are the ones which depend on the environment / sensors.
Those are: `input_shapes` and `output_shapes`.
Those are: `input_features` and `output_features`.
Notes on the inputs and outputs:
- "observation.state" is required as an input key.
@@ -48,21 +48,12 @@ class DiffusionConfig(PreTrainedConfig):
horizon: Diffusion model action prediction size as detailed in `DiffusionPolicy.select_action`.
n_action_steps: The number of action steps to run in the environment for one invocation of the policy.
See `DiffusionPolicy.select_action` for more details.
input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents
the input data name, and the value is a list indicating the dimensions of the corresponding data.
For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96],
indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't
include batch dimension or temporal dimension.
output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents
the output data name, and the value is a list indicating the dimensions of the corresponding data.
For example, "action" refers to an output shape of [14], indicating 14-dimensional actions.
Importantly, `output_shapes` doesn't include batch dimension or temporal dimension.
input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"),
and the value specifies the normalization mode to apply. The two available modes are "mean_std"
which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a
[-1, 1] range.
output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the
original scale. Note that this is also used for normalizing the training targets.
input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents
the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents
the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to
a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX)
vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit
within the image size. If None, no cropping is done.