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https://github.com/huggingface/lerobot.git
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2dd366436e
* Add GymHILAdapterProcessorStep for gym-hil environment integration * Fix action features in control loop for None teleop device with gym-hil * Finalize dataset before pushing to hub for visualization on the hub * Fix neutral action for gripper * fix pre-commit
106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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from lerobot.configs.types import PipelineFeatureType, PolicyFeature
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from .converters import to_tensor
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from .core import EnvAction, EnvTransition, PolicyAction
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from .hil_processor import TELEOP_ACTION_KEY
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from .pipeline import ActionProcessorStep, ProcessorStep, ProcessorStepRegistry
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@ProcessorStepRegistry.register("torch2numpy_action_processor")
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@dataclass
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class Torch2NumpyActionProcessorStep(ActionProcessorStep):
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"""
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Converts a PyTorch tensor action to a NumPy array.
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This step is useful when the output of a policy (typically a torch.Tensor)
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needs to be passed to an environment or component that expects a NumPy array.
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Attributes:
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squeeze_batch_dim: If True, removes the first dimension of the array
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if it is of size 1. This is useful for converting a
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batched action of size (1, D) to a single action of size (D,).
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"""
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squeeze_batch_dim: bool = True
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def action(self, action: PolicyAction) -> EnvAction:
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if not isinstance(action, PolicyAction):
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raise TypeError(
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f"Expected PolicyAction or None, got {type(action).__name__}. "
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"Use appropriate processor for non-tensor actions."
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)
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numpy_action = action.detach().cpu().numpy()
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# Remove batch dimensions but preserve action dimensions.
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# Only squeeze if there's a batch dimension (first dim == 1).
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if (
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self.squeeze_batch_dim
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and numpy_action.shape
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and len(numpy_action.shape) > 1
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and numpy_action.shape[0] == 1
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):
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numpy_action = numpy_action.squeeze(0)
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return numpy_action
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def transform_features(
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self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
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) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
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return features
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@ProcessorStepRegistry.register("numpy2torch_action_processor")
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@dataclass
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class Numpy2TorchActionProcessorStep(ProcessorStep):
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"""Converts a NumPy array action to a PyTorch tensor when action is present."""
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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"""Converts numpy action to torch tensor if action exists, otherwise passes through."""
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from .core import TransitionKey
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self._current_transition = transition.copy()
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new_transition = self._current_transition
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action = new_transition.get(TransitionKey.ACTION)
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if action is not None:
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if not isinstance(action, EnvAction):
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raise TypeError(
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f"Expected np.ndarray or None, got {type(action).__name__}. "
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"Use appropriate processor for non-tensor actions."
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)
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torch_action = to_tensor(action, dtype=None) # Preserve original dtype
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new_transition[TransitionKey.ACTION] = torch_action
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complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
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if TELEOP_ACTION_KEY in complementary_data:
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teleop_action = complementary_data[TELEOP_ACTION_KEY]
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if isinstance(teleop_action, EnvAction):
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complementary_data[TELEOP_ACTION_KEY] = to_tensor(teleop_action)
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new_transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
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return new_transition
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def transform_features(
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self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
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) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
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return features
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