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Make it possible to unset policy features

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This is necessary to train pre-trained policies on new datasets so that the
features are inferred from the new dataset and not from the pretrained
policy.
This commit is contained in:
nemo
2025-12-05 12:48:07 +01:00
parent 75266860aa
commit f5bf6bb028
1 changed files with 3 additions and 2 deletions
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src/lerobot/configs/policies.py
+3 -2
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@@ -55,8 +55,9 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC): # type: igno
n_obs_steps: int = 1 n_obs_steps: int = 1
input_features: dict[str, PolicyFeature] = field(default_factory=dict) # `input_features` can be set to None/null in order to infer those values from the dataset.
output_features: dict[str, PolicyFeature] = field(default_factory=dict) input_features: dict[str, PolicyFeature] | None = field(default_factory=dict)
output_features: dict[str, PolicyFeature] | None = field(default_factory=dict)
device: str | None = None # e.g. "cuda", "cuda:0", "cpu", or "mps" device: str | None = None # e.g. "cuda", "cuda:0", "cpu", or "mps"
# `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP, # `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP,