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lerobot-clone/src/lerobot/processor/gym_action_processor.py

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Python

#! /usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
from dataclasses import dataclass
import numpy as np
import torch
from lerobot.configs.types import PolicyFeature
from .converters import to_tensor
from .pipeline import ActionProcessor, ProcessorStepRegistry
@ProcessorStepRegistry.register("torch2numpy_action_processor")
@dataclass
class Torch2NumpyActionProcessor(ActionProcessor):
"""Convert PyTorch tensor actions to NumPy arrays."""
squeeze_batch_dim: bool = True
def action(self, action: torch.Tensor) -> np.ndarray:
if not isinstance(action, torch.Tensor):
raise TypeError(
f"Expected torch.Tensor or None, got {type(action).__name__}. "
"Use appropriate processor for non-tensor actions."
)
numpy_action = action.detach().cpu().numpy()
# Remove batch dimensions but preserve action dimensions
# Only squeeze if there's a batch dimension (first dim == 1)
if (
self.squeeze_batch_dim
and numpy_action.shape
and len(numpy_action.shape) > 1
and numpy_action.shape[0] == 1
):
numpy_action = numpy_action.squeeze(0)
return numpy_action
def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
return features
@ProcessorStepRegistry.register("numpy2torch_action_processor")
@dataclass
class Numpy2TorchActionProcessor(ActionProcessor):
"""Convert NumPy array action to PyTorch tensor."""
def action(self, action: np.ndarray) -> torch.Tensor:
if not isinstance(action, np.ndarray):
raise TypeError(
f"Expected np.ndarray or None, got {type(action).__name__}. "
"Use appropriate processor for non-tensor actions."
)
torch_action = to_tensor(action, dtype=None) # Preserve original dtype
return torch_action
def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
return features