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Merge branch 'main' into docs/add-lelab

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This commit is contained in:
Nikodem Bartnik
2026-06-03 13:40:23 +02:00
committed by GitHub
parent 8da7362204 19fe315971
commit 41c1b71f57
3 changed files with 19 additions and 18 deletions
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6
src/lerobot/configs/train.py
View File

@@ -177,6 +177,12 @@ class TrainPipelineConfig(HubMixin):
)
active_cfg = self.trainable_config
if self.rename_map and active_cfg.pretrained_path is None:
raise ValueError(
"`rename_map` requires a pretrained policy checkpoint. "
"Fresh initialization derives feature names from the current dataset, so no rename is applied."
)
if not self.job_name:
if self.env is None:
self.job_name = f"{active_cfg.type}"

2
src/lerobot/processor/relative_action_processor.py
View File

@@ -81,7 +81,7 @@ def to_absolute_actions(actions: Tensor, state: Tensor, mask: Sequence[bool]) ->
return actions
@ProcessorStepRegistry.register("delta_actions_processor")
@ProcessorStepRegistry.register("relative_actions_processor")
@dataclass
class RelativeActionsProcessorStep(ProcessorStep):
"""Converts absolute actions to relative actions (action -= state) for masked dimensions.

29
src/lerobot/scripts/lerobot_train.py
View File

@@ -292,19 +292,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
active_cfg = cfg.trainable_config
processor_pretrained_path = active_cfg.pretrained_path
if (
getattr(active_cfg, "use_relative_actions", False)
and processor_pretrained_path is not None
and not cfg.resume
):
logging.warning(
"use_relative_actions=true with pretrained processors can skip relative transforms if "
"the checkpoint processors do not define them. Building processors from current policy config."
)
processor_pretrained_path = None
processor_kwargs = {}
postprocessor_kwargs = {}
if (processor_pretrained_path and not cfg.resume) or not processor_pretrained_path:
processor_kwargs["dataset_stats"] = dataset.meta.stats
@@ -312,24 +301,31 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
processor_kwargs["dataset_meta"] = dataset.meta
if not cfg.is_reward_model_training and processor_pretrained_path is not None:
processor_kwargs["preprocessor_overrides"] = {
preprocessor_overrides = {
"device_processor": {"device": device.type},
"normalizer_processor": {
"stats": dataset.meta.stats,
"features": {**policy.config.input_features, **policy.config.output_features},
"norm_map": policy.config.normalization_mapping,
},
"rename_observations_processor": {"rename_map": cfg.rename_map},
}
processor_kwargs["preprocessor_overrides"]["rename_observations_processor"] = {
"rename_map": cfg.rename_map
}
postprocessor_kwargs["postprocessor_overrides"] = {
postprocessor_overrides = {
"unnormalizer_processor": {
"stats": dataset.meta.stats,
"features": policy.config.output_features,
"norm_map": policy.config.normalization_mapping,
},
}
if getattr(active_cfg, "use_relative_actions", False):
preprocessor_overrides["relative_actions_processor"] = {
"enabled": True,
"exclude_joints": getattr(active_cfg, "relative_exclude_joints", []),
"action_names": getattr(active_cfg, "action_feature_names", None),
}
postprocessor_overrides["absolute_actions_processor"] = {"enabled": True}
processor_kwargs["preprocessor_overrides"] = preprocessor_overrides
processor_kwargs["postprocessor_overrides"] = postprocessor_overrides
if cfg.is_reward_model_training:
preprocessor, postprocessor = make_reward_pre_post_processors(
@@ -341,7 +337,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
policy_cfg=cfg.policy,
pretrained_path=processor_pretrained_path,
**processor_kwargs,
**postprocessor_kwargs,
)
if is_main_process: