refactor(normalization): Remove unused state dict transformation methods and streamline imports

- Eliminated the _transform_state_dict_keys and _load_as_safetensor methods from PI0Policy, simplifying the model loading process.
- Cleaned up imports in modeling_pi0.py by removing log_model_loading_keys and init_logging.
- Updated TDMPCPolicy and VQBeTPolicy to handle action removal from batches during offline evaluation.
- Introduced hotswap_stats function in normalize_processor.py to update normalization statistics dynamically, with corresponding tests to ensure functionality.

src/lerobot/policies/pi0/modeling_pi0.py

@@ -65,8 +65,7 @@ from lerobot.policies.pi0.paligemma_with_expert import (
     PaliGemmaWithExpertModel,
 )
 from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.policies.utils import log_model_loading_keys
-from lerobot.utils.utils import get_safe_dtype, init_logging
+from lerobot.utils.utils import get_safe_dtype
 
 
 def create_sinusoidal_pos_embedding(
@@ -245,99 +244,6 @@ class PI0Policy(PreTrainedPolicy):
         """This should be called whenever the environment is reset."""
         self._action_queue = deque([], maxlen=self.config.n_action_steps)
 
-    @classmethod
-    def _transform_state_dict_keys(cls, state_dict: dict) -> dict:
-        """
-        Transform state dict keys to match expected model structure.
-
-        Transformations:
-        - model.paligemma_with_expert.paligemma.language_model.lm_head ->
-          model.paligemma_with_expert.paligemma.lm_head
-        - model.paligemma_with_expert.paligemma.language_model.model ->
-          model.paligemma_with_expert.paligemma.model.language_model
-        - model.paligemma_with_expert.paligemma.vision_tower ->
-          model.paligemma_with_expert.paligemma.model.vision_tower
-        - model.paligemma_with_expert.paligemma.multi_modal_projector ->
-          model.paligemma_with_expert.paligemma.model.multi_modal_projector
-
-        Also handles tied weights between lm_head.weight and
-        embed_tokens.weight.
-        """
-        import re
-
-        transformed_dict = {}
-
-        transformations = [
-            (
-                re.compile(r"\.paligemma_with_expert\.paligemma\.language_model\.lm_head"),
-                ".paligemma_with_expert.paligemma.lm_head",
-            ),
-            (
-                re.compile(r"\.paligemma_with_expert\.paligemma\.language_model\.model"),
-                ".paligemma_with_expert.paligemma.model.language_model",
-            ),
-            (
-                re.compile(r"\.paligemma_with_expert\.paligemma\.vision_tower"),
-                ".paligemma_with_expert.paligemma.model.vision_tower",
-            ),
-            (
-                re.compile(r"\.paligemma_with_expert\.paligemma\.multi_modal_projector"),
-                ".paligemma_with_expert.paligemma.model.multi_modal_projector",
-            ),
-        ]
-
-        for key, value in state_dict.items():
-            new_key = key
-            for pattern, replacement in transformations:
-                new_key = pattern.sub(replacement, new_key)
-            transformed_dict[new_key] = value
-
-        # Handle tied weights: lm_head.weight and embed_tokens.weight share memory
-        lm_head_key = None
-        embed_tokens_key = None
-
-        for key in transformed_dict:
-            if key.endswith(".paligemma_with_expert.paligemma.lm_head.weight"):
-                lm_head_key = key
-            elif key.endswith(".paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"):
-                embed_tokens_key = key
-            if lm_head_key and embed_tokens_key:
-                break
-
-        if lm_head_key and not embed_tokens_key:
-            embed_tokens_key = lm_head_key.replace(
-                ".lm_head.weight", ".model.language_model.embed_tokens.weight"
-            )
-            transformed_dict[embed_tokens_key] = transformed_dict[lm_head_key]
-        elif embed_tokens_key and not lm_head_key:
-            lm_head_key = embed_tokens_key.replace(
-                ".model.language_model.embed_tokens.weight", ".lm_head.weight"
-            )
-            transformed_dict[lm_head_key] = transformed_dict[embed_tokens_key]
-
-        return transformed_dict
-
-    @classmethod
-    def _load_as_safetensor(
-        cls, model: "PI0Policy", model_file: str, map_location: str, strict: bool
-    ) -> "PI0Policy":
-        """Override to apply key transformations before loading."""
-        from safetensors.torch import load_file
-
-        init_logging()
-        # Load the state dict from file safely
-        state_dict = load_file(model_file, device=map_location)
-
-        # Apply key transformations
-        transformed_state_dict = cls._transform_state_dict_keys(state_dict)
-
-        # Load the transformed state dict
-        msg = model.load_state_dict(transformed_state_dict, strict=strict)
-
-        # Log message
-        log_model_loading_keys(msg.missing_keys, msg.unexpected_keys)
-        return model
-
     def get_optim_params(self) -> dict:
         return self.parameters()
 

src/lerobot/policies/tdmpc/modeling_tdmpc.py

@@ -141,6 +141,9 @@ class TDMPCPolicy(PreTrainedPolicy):
         if self.config.image_features:
             batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
             batch[OBS_IMAGE] = batch[next(iter(self.config.image_features))]
+        # NOTE: for offline evaluation, we have action in the batch, so we need to pop it out
+        if ACTION in batch:
+            batch.pop(ACTION)
 
         self._queues = populate_queues(self._queues, batch)
 

src/lerobot/policies/vqbet/modeling_vqbet.py

@@ -134,6 +134,9 @@ class VQBeTPolicy(PreTrainedPolicy):
         batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
         # NOTE: It's important that this happens after stacking the images into a single key.
         batch["observation.images"] = torch.stack([batch[key] for key in self.config.image_features], dim=-4)
+        # NOTE: for offline evaluation, we have action in the batch, so we need to pop it out
+        if ACTION in batch:
+            batch.pop(ACTION)
 
         self._queues = populate_queues(self._queues, batch)
 

src/lerobot/processor/__init__.py

@@ -16,7 +16,7 @@
 
 from .batch_processor import ToBatchProcessor
 from .device_processor import DeviceProcessor
-from .normalize_processor import NormalizerProcessor, UnnormalizerProcessor
+from .normalize_processor import NormalizerProcessor, UnnormalizerProcessor, hotswap_stats
 from .observation_processor import VanillaObservationProcessor
 from .pipeline import (
     ActionProcessor,
@@ -43,6 +43,7 @@ __all__ = [
     "InfoProcessor",
     "NormalizerProcessor",
     "UnnormalizerProcessor",
+    "hotswap_stats",
     "ObservationProcessor",
     "ProcessorStep",
     "ProcessorStepRegistry",

src/lerobot/processor/normalize_processor.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
+
 from collections.abc import Mapping
+from copy import deepcopy
 from dataclasses import dataclass, field
 from typing import Any
 
@@ -10,7 +12,7 @@ from torch import Tensor
 
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.processor.pipeline import EnvTransition, ProcessorStepRegistry, TransitionKey
+from lerobot.processor.pipeline import EnvTransition, ProcessorStepRegistry, TransitionKey, RobotProcessor
 
 
 def _convert_stats_to_tensors(stats: dict[str, dict[str, Any]]) -> dict[str, dict[str, Tensor]]:
@@ -402,3 +404,13 @@ class UnnormalizerProcessor:
 
     def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
         return features
+
+
+def hotswap_stats(robot_processor: RobotProcessor, stats: dict[str, dict[str, Any]]) -> RobotProcessor:
+    robot_processor = deepcopy(robot_processor)
+    for step in robot_processor.steps:
+        if isinstance(step, NormalizerProcessor) or isinstance(step, UnnormalizerProcessor):
+            step: NormalizerProcessor | UnnormalizerProcessor
+            step.stats = stats
+            step._tensor_stats = _convert_stats_to_tensors(stats)
+    return robot_processor

tests/processor/test_normalize_processor.py

@@ -24,8 +24,9 @@ from lerobot.processor.normalize_processor import (
     NormalizerProcessor,
     UnnormalizerProcessor,
     _convert_stats_to_tensors,
+    hotswap_stats,
 )
-from lerobot.processor.pipeline import RobotProcessor, TransitionKey
+from lerobot.processor.pipeline import IdentityProcessor, RobotProcessor, TransitionKey
 
 
 def create_transition(
@@ -953,3 +954,377 @@ def test_unsupported_normalization_mode_error():
 
     with pytest.raises(ValueError, match="Unsupported normalization mode"):
         normalizer(transition)
+
+
+def test_hotswap_stats_basic_functionality():
+    """Test that hotswap_stats correctly updates stats in normalizer/unnormalizer steps."""
+    # Create initial stats
+    initial_stats = {
+        "observation.image": {"mean": np.array([0.5, 0.5, 0.5]), "std": np.array([0.2, 0.2, 0.2])},
+        "action": {"mean": np.array([0.0, 0.0]), "std": np.array([1.0, 1.0])},
+    }
+
+    # Create new stats for hotswapping
+    new_stats = {
+        "observation.image": {"mean": np.array([0.3, 0.3, 0.3]), "std": np.array([0.1, 0.1, 0.1])},
+        "action": {"mean": np.array([0.1, 0.1]), "std": np.array([0.5, 0.5])},
+    }
+
+    # Create features and norm_map
+    features = {
+        "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)),
+        "action": PolicyFeature(type=FeatureType.ACTION, shape=(2,)),
+    }
+    norm_map = {
+        FeatureType.VISUAL: NormalizationMode.MEAN_STD,
+        FeatureType.ACTION: NormalizationMode.MEAN_STD,
+    }
+
+    # Create processors
+    normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
+    unnormalizer = UnnormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
+    identity = IdentityProcessor()
+
+    # Create robot processor
+    robot_processor = RobotProcessor(steps=[normalizer, unnormalizer, identity])
+
+    # Hotswap stats
+    new_processor = hotswap_stats(robot_processor, new_stats)
+
+    # Check that normalizer and unnormalizer have new stats
+    assert new_processor.steps[0].stats == new_stats
+    assert new_processor.steps[1].stats == new_stats
+
+    # Check that tensor stats are updated correctly
+    expected_tensor_stats = _convert_stats_to_tensors(new_stats)
+    for key in expected_tensor_stats:
+        for stat_name in expected_tensor_stats[key]:
+            torch.testing.assert_close(
+                new_processor.steps[0]._tensor_stats[key][stat_name], expected_tensor_stats[key][stat_name]
+            )
+            torch.testing.assert_close(
+                new_processor.steps[1]._tensor_stats[key][stat_name], expected_tensor_stats[key][stat_name]
+            )
+
+
+def test_hotswap_stats_deep_copy():
+    """Test that hotswap_stats creates a deep copy and doesn't modify the original processor."""
+    initial_stats = {
+        "observation.image": {"mean": np.array([0.5, 0.5, 0.5]), "std": np.array([0.2, 0.2, 0.2])},
+    }
+
+    new_stats = {
+        "observation.image": {"mean": np.array([0.3, 0.3, 0.3]), "std": np.array([0.1, 0.1, 0.1])},
+    }
+
+    features = {
+        "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)),
+    }
+    norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
+
+    normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
+    original_processor = RobotProcessor(steps=[normalizer])
+
+    # Store reference to original stats
+    original_stats_reference = original_processor.steps[0].stats
+    original_tensor_stats_reference = original_processor.steps[0]._tensor_stats
+
+    # Hotswap stats
+    new_processor = hotswap_stats(original_processor, new_stats)
+
+    # Original processor should be unchanged
+    assert original_processor.steps[0].stats is original_stats_reference
+    assert original_processor.steps[0]._tensor_stats is original_tensor_stats_reference
+    assert original_processor.steps[0].stats == initial_stats
+
+    # New processor should have new stats
+    assert new_processor.steps[0].stats == new_stats
+    assert new_processor.steps[0].stats is not original_stats_reference
+
+    # Processors should be different objects
+    assert new_processor is not original_processor
+    assert new_processor.steps[0] is not original_processor.steps[0]
+
+
+def test_hotswap_stats_only_affects_normalizer_steps():
+    """Test that hotswap_stats only modifies NormalizerProcessor and UnnormalizerProcessor steps."""
+    stats = {
+        "observation.image": {"mean": np.array([0.5]), "std": np.array([0.2])},
+    }
+
+    new_stats = {
+        "observation.image": {"mean": np.array([0.3]), "std": np.array([0.1])},
+    }
+
+    features = {
+        "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)),
+    }
+    norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
+
+    # Create mixed steps
+    normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=stats)
+    unnormalizer = UnnormalizerProcessor(features=features, norm_map=norm_map, stats=stats)
+    identity = IdentityProcessor()
+
+    robot_processor = RobotProcessor(steps=[normalizer, identity, unnormalizer])
+
+    # Hotswap stats
+    new_processor = hotswap_stats(robot_processor, new_stats)
+
+    # Check that only normalizer and unnormalizer steps are affected
+    assert new_processor.steps[0].stats == new_stats  # normalizer
+    assert new_processor.steps[2].stats == new_stats  # unnormalizer
+
+    # Identity processor should remain unchanged (and it doesn't have stats attribute)
+    assert not hasattr(new_processor.steps[1], "stats")
+
+
+def test_hotswap_stats_empty_stats():
+    """Test hotswap_stats with empty stats dictionary."""
+    initial_stats = {
+        "observation.image": {"mean": np.array([0.5]), "std": np.array([0.2])},
+    }
+
+    empty_stats = {}
+
+    features = {
+        "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)),
+    }
+    norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
+
+    normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
+    robot_processor = RobotProcessor(steps=[normalizer])
+
+    # Hotswap with empty stats
+    new_processor = hotswap_stats(robot_processor, empty_stats)
+
+    # Should update to empty stats
+    assert new_processor.steps[0].stats == empty_stats
+    assert new_processor.steps[0]._tensor_stats == {}
+
+
+def test_hotswap_stats_no_normalizer_steps():
+    """Test hotswap_stats with a processor that has no normalizer/unnormalizer steps."""
+    stats = {
+        "observation.image": {"mean": np.array([0.5]), "std": np.array([0.2])},
+    }
+
+    # Create processor with only identity steps
+    robot_processor = RobotProcessor(steps=[IdentityProcessor(), IdentityProcessor()])
+
+    # Hotswap stats - should work without error
+    new_processor = hotswap_stats(robot_processor, stats)
+
+    # Should return a different object (deep copy)
+    assert new_processor is not robot_processor
+
+    # Steps should be deep copied but unchanged
+    assert len(new_processor.steps) == len(robot_processor.steps)
+    for i, step in enumerate(new_processor.steps):
+        assert step is not robot_processor.steps[i]  # Different objects
+        assert isinstance(step, type(robot_processor.steps[i]))  # Same type
+
+
+def test_hotswap_stats_preserves_other_attributes():
+    """Test that hotswap_stats preserves other processor attributes like features and norm_map."""
+    initial_stats = {
+        "observation.image": {"mean": np.array([0.5]), "std": np.array([0.2])},
+    }
+
+    new_stats = {
+        "observation.image": {"mean": np.array([0.3]), "std": np.array([0.1])},
+    }
+
+    features = {
+        "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)),
+    }
+    norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
+    normalize_keys = {"observation.image"}
+    eps = 1e-6
+
+    normalizer = NormalizerProcessor(
+        features=features, norm_map=norm_map, stats=initial_stats, normalize_keys=normalize_keys, eps=eps
+    )
+    robot_processor = RobotProcessor(steps=[normalizer])
+
+    # Hotswap stats
+    new_processor = hotswap_stats(robot_processor, new_stats)
+
+    # Check that other attributes are preserved
+    new_normalizer = new_processor.steps[0]
+    assert new_normalizer.features == features
+    assert new_normalizer.norm_map == norm_map
+    assert new_normalizer.normalize_keys == normalize_keys
+    assert new_normalizer.eps == eps
+
+    # But stats should be updated
+    assert new_normalizer.stats == new_stats
+
+
+def test_hotswap_stats_multiple_normalizer_types():
+    """Test hotswap_stats with multiple normalizer and unnormalizer steps."""
+    initial_stats = {
+        "observation.image": {"mean": np.array([0.5]), "std": np.array([0.2])},
+        "action": {"min": np.array([-1.0]), "max": np.array([1.0])},
+    }
+
+    new_stats = {
+        "observation.image": {"mean": np.array([0.3]), "std": np.array([0.1])},
+        "action": {"min": np.array([-2.0]), "max": np.array([2.0])},
+    }
+
+    features = {
+        "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)),
+        "action": PolicyFeature(type=FeatureType.ACTION, shape=(1,)),
+    }
+    norm_map = {
+        FeatureType.VISUAL: NormalizationMode.MEAN_STD,
+        FeatureType.ACTION: NormalizationMode.MIN_MAX,
+    }
+
+    # Create multiple normalizers and unnormalizers
+    normalizer1 = NormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
+    normalizer2 = NormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
+    unnormalizer1 = UnnormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
+    unnormalizer2 = UnnormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
+
+    robot_processor = RobotProcessor(steps=[normalizer1, unnormalizer1, normalizer2, unnormalizer2])
+
+    # Hotswap stats
+    new_processor = hotswap_stats(robot_processor, new_stats)
+
+    # All normalizer/unnormalizer steps should be updated
+    for step in new_processor.steps:
+        assert step.stats == new_stats
+
+        # Check tensor stats conversion
+        expected_tensor_stats = _convert_stats_to_tensors(new_stats)
+        for key in expected_tensor_stats:
+            for stat_name in expected_tensor_stats[key]:
+                torch.testing.assert_close(
+                    step._tensor_stats[key][stat_name], expected_tensor_stats[key][stat_name]
+                )
+
+
+def test_hotswap_stats_with_different_data_types():
+    """Test hotswap_stats with various data types in stats."""
+    initial_stats = {
+        "observation.image": {"mean": np.array([0.5]), "std": np.array([0.2])},
+    }
+
+    # New stats with different data types (int, float, list, tuple)
+    new_stats = {
+        "observation.image": {
+            "mean": [0.3, 0.4, 0.5],  # list
+            "std": (0.1, 0.2, 0.3),  # tuple
+            "min": 0,  # int
+            "max": 1.0,  # float
+        },
+        "action": {
+            "mean": np.array([0.1, 0.2]),  # numpy array
+            "std": torch.tensor([0.5, 0.6]),  # torch tensor
+        },
+    }
+
+    features = {
+        "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)),
+        "action": PolicyFeature(type=FeatureType.ACTION, shape=(2,)),
+    }
+    norm_map = {
+        FeatureType.VISUAL: NormalizationMode.MEAN_STD,
+        FeatureType.ACTION: NormalizationMode.MEAN_STD,
+    }
+
+    normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
+    robot_processor = RobotProcessor(steps=[normalizer])
+
+    # Hotswap stats
+    new_processor = hotswap_stats(robot_processor, new_stats)
+
+    # Check that stats are updated
+    assert new_processor.steps[0].stats == new_stats
+
+    # Check that tensor conversion worked correctly
+    tensor_stats = new_processor.steps[0]._tensor_stats
+    assert isinstance(tensor_stats["observation.image"]["mean"], torch.Tensor)
+    assert isinstance(tensor_stats["observation.image"]["std"], torch.Tensor)
+    assert isinstance(tensor_stats["observation.image"]["min"], torch.Tensor)
+    assert isinstance(tensor_stats["observation.image"]["max"], torch.Tensor)
+    assert isinstance(tensor_stats["action"]["mean"], torch.Tensor)
+    assert isinstance(tensor_stats["action"]["std"], torch.Tensor)
+
+    # Check values
+    torch.testing.assert_close(tensor_stats["observation.image"]["mean"], torch.tensor([0.3, 0.4, 0.5]))
+    torch.testing.assert_close(tensor_stats["observation.image"]["std"], torch.tensor([0.1, 0.2, 0.3]))
+    torch.testing.assert_close(tensor_stats["observation.image"]["min"], torch.tensor(0.0))
+    torch.testing.assert_close(tensor_stats["observation.image"]["max"], torch.tensor(1.0))
+
+
+def test_hotswap_stats_functional_test():
+    """Test that hotswapped processor actually works functionally."""
+    # Create test data
+    observation = {
+        "observation.image": torch.tensor([[[0.6, 0.7], [0.8, 0.9]], [[0.5, 0.6], [0.7, 0.8]]]),
+    }
+    action = torch.tensor([0.5, -0.5])
+    transition = create_transition(observation=observation, action=action)
+
+    # Initial stats
+    initial_stats = {
+        "observation.image": {"mean": np.array([0.5, 0.4]), "std": np.array([0.2, 0.3])},
+        "action": {"mean": np.array([0.0, 0.0]), "std": np.array([1.0, 1.0])},
+    }
+
+    # New stats
+    new_stats = {
+        "observation.image": {"mean": np.array([0.3, 0.2]), "std": np.array([0.1, 0.2])},
+        "action": {"mean": np.array([0.1, -0.1]), "std": np.array([0.5, 0.5])},
+    }
+
+    features = {
+        "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(2, 2, 2)),
+        "action": PolicyFeature(type=FeatureType.ACTION, shape=(2,)),
+    }
+    norm_map = {
+        FeatureType.VISUAL: NormalizationMode.MEAN_STD,
+        FeatureType.ACTION: NormalizationMode.MEAN_STD,
+    }
+
+    # Create original processor
+    normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
+    original_processor = RobotProcessor(steps=[normalizer])
+
+    # Process with original stats
+    original_result = original_processor(transition)
+
+    # Hotswap stats
+    new_processor = hotswap_stats(original_processor, new_stats)
+
+    # Process with new stats
+    new_result = new_processor(transition)
+
+    # Results should be different since normalization changed
+    assert not torch.allclose(
+        original_result["observation"]["observation.image"],
+        new_result["observation"]["observation.image"],
+        rtol=1e-3,
+        atol=1e-3,
+    )
+    assert not torch.allclose(original_result["action"], new_result["action"], rtol=1e-3, atol=1e-3)
+
+    # Verify that the new processor is actually using the new stats by checking internal state
+    assert new_processor.steps[0].stats == new_stats
+    assert torch.allclose(
+        new_processor.steps[0]._tensor_stats["observation.image"]["mean"], torch.tensor([0.3, 0.2])
+    )
+    assert torch.allclose(
+        new_processor.steps[0]._tensor_stats["observation.image"]["std"], torch.tensor([0.1, 0.2])
+    )
+    assert torch.allclose(new_processor.steps[0]._tensor_stats["action"]["mean"], torch.tensor([0.1, -0.1]))
+    assert torch.allclose(new_processor.steps[0]._tensor_stats["action"]["std"], torch.tensor([0.5, 0.5]))
+
+    # Test that normalization actually happens (output should not equal input)
+    assert not torch.allclose(
+        new_result["observation"]["observation.image"], observation["observation.image"]
+    )
+    assert not torch.allclose(new_result["action"], action)